Datasets:

WINGNUS
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ACL-OCL

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	"title": "Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering",
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	"text": "Welcome to The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems.",
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	"text": "SemEval-2017 is the eleventh workshop in the series of International Workshops on Semantic Evaluation. The first three workshops, SensEval-1 (1998), SensEval-2 (2001), and SensEval-3 (2004) , focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and the subsequent SemEval workshops evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval-2017 were proposed in 2016.",
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	"text": "SensEval-2 (2001), and",
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	"text": "SemEval-2017 was co-located with the 55th annual meeting of the Association for Computational Linguistics (ACL'2017) in Vancouver, Canada. It included the following 12 shared tasks organized in three tracks: This volume contains both Task Description papers that describe each of the above tasks and System Description papers that describe the systems that participated in the above tasks. A total of 12 task description papers and 169 system description papers are included in this volume.",
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	"text": "We are grateful to all task organizers as well as the large number of participants whose enthusiastic participation has made SemEval once again a successful event. We are thankful to the task organizers who also served as area chairs, and to task organizers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. We also thank the ACL 2017 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsor, the ACL Special Interest Group on the Lexicon (SIGLEX). ",
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	"text": "CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity J\u00e9r\u00e9my Ferrero, Laurent Besacier, Didier Schwab and Fr\u00e9d\u00e9ric Agn\u00e8s. . . . . . . . . . . . . . . . . . . . . . .109 UdL at SemEval-2017 Farag . . . . . . . . . . . . . . . . . . . . . . 125 HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity Yang Shao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting El Moatez Billah NAGOUDI, J\u00e9r\u00e9my Ferrero and Didier Schwab . . . . . . . . . . . . . . . . . . . . . . . . . . 134 OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity Martyna\u015apiewak, Piotr Sobecki and Daniel Kara\u015b . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Lump at SemEval-2017 Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 x QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering ForumsMarwan Torki, Maram Hasanain and Tamer Elsayed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task Guoshun Wu, Yixuan Sheng, Man Lan and Yuanbin Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA Surya Agustian and Hiroya Takamura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection Byron Galbraith, Bhanu Pratap and Daniel Shank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter Xiwu Han and Gregory Toner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Duluth at SemEval-2017 Task 6: Language Models in Humor Detection Xinru Yan and Ted Pedersen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison Christos Baziotis, Nikos Pelekis and Christos Doulkeridis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 TakeLab at SemEval-2017 Task ",
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	"ref_id": "b0",
	"title": "RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity Ergun Bi\u00e7ici",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
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	"raw_text": "RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity Ergun Bi\u00e7ici . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203",
	"links": null
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	"ref_id": "b1",
	"title": "Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity Ignacio Arroyo-Fern\u00e1ndez and Ivan Vladimir Meza Ruiz",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
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	"links": null
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	"BIBREF2": {
	"ref_id": "b2",
	"title": "and 2: Lexical and semantic features in word and textual similarity Pedro Fialho",
	"authors": [
	{
	"first": "Lu\u00edsa",
	"middle": [],
	"last": "Hugo Patinho Rodrigues",
	"suffix": ""
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	{
	"first": "Paulo",
	"middle": [],
	"last": "Coheur",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". . . . . . . . . . . . . ."
	],
	"last": "Quaresma",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
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	"links": null
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	"BIBREF3": {
	"ref_id": "b3",
	"title": "HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity Junqing He",
	"authors": [
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	"last": "Wu",
	"suffix": ""
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	{
	"first": "Xuemin",
	"middle": [],
	"last": "Zhao",
	"suffix": ""
	},
	{
	"first": "Yonghong",
	"middle": [],
	"last": "Yan",
	"suffix": ""
	},
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	"first": ".",
	"middle": [
	"."
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	"last": "",
	"suffix": ""
	}
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	"raw_text": "HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity Junqing He, Long Wu, Xuemin Zhao and Yonghong Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220",
	"links": null
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	"BIBREF4": {
	"ref_id": "b4",
	"title": "Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts Pablo Gamallo",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
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	"other_ids": {},
	"num": null,
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	"raw_text": "Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency- Based Contexts Pablo Gamallo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226",
	"links": null
	},
	"BIBREF5": {
	"ref_id": "b5",
	"title": "Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity Josu\u00e9 Melka and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity Josu\u00e9 Melka and Gilles Bernard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230",
	"links": null
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	"BIBREF6": {
	"ref_id": "b6",
	"title": "QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base Fanqing Meng",
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	"last": "Lu",
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	{
	"first": "Yuteng",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	},
	{
	"first": "Ping",
	"middle": [],
	"last": "Jian",
	"suffix": ""
	},
	{
	"first": "Shumin",
	"middle": [],
	"last": "Shi",
	"suffix": ""
	},
	{
	"first": "Heyan",
	"middle": [
	". . ."
	],
	"last": "Huang",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
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	"issue": "",
	"pages": "",
	"other_ids": {},
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	"links": null
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	"BIBREF7": {
	"ref_id": "b7",
	"title": "Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list Sergio Jimenez",
	"authors": [
	{
	"first": "Jorge",
	"middle": [],
	"last": "Gaitan",
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	{
	"first": ".",
	"middle": [
	"."
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	"last": "Segura",
	"suffix": ""
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	],
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	"venue": "",
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	"issue": "",
	"pages": "",
	"other_ids": {},
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	"links": null
	},
	"BIBREF8": {
	"ref_id": "b8",
	"title": "MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach Enrico Mensa",
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	"last": "Radicioni",
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	"links": null
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	"BIBREF9": {
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	"links": null
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	"BIBREF10": {
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	"authors": [],
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	"links": null
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	"links": null
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	"title": "Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity R\u01cezvan-Gabriel Rotari, Ionut Hulub",
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	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic Yassine El Adlouni, Imane Lahbari, Horacio Rodriguez, Mohammed Meknassi, Said Ouatik El Alaoui and Noureddine Ennahnahi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275",
	"links": null
	},
	"BIBREF16": {
	"ref_id": "b16",
	"title": "ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation Llu\u00eds",
	"authors": [
	{
	"first": "F",
	"middle": [],
	"last": "Hurtado",
	"suffix": ""
	},
	{
	"first": "Encarna",
	"middle": [],
	"last": "Segarra",
	"suffix": ""
	},
	{
	"first": "Ferran",
	"middle": [],
	"last": "Pla",
	"suffix": ""
	},
	{
	"first": "Pascual",
	"middle": [],
	"last": "Carrasco",
	"suffix": ""
	},
	{
	"first": "Jos\u00e9-\u00c1ngel",
	"middle": [
	"."
	],
	"last": "Gonz\u00e1lez",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation Llu\u00eds-F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco and Jos\u00e9-\u00c1ngel Gonz\u00e1lez . . . . 440",
	"links": null
	},
	"BIBREF17": {
	"ref_id": "b17",
	"title": "Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings Dieke Oele and Kilian Evang",
	"authors": [],
	"year": null,
	"venue": "Global vs",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings Dieke Oele and Kilian Evang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444",
	"links": null
	},
	"BIBREF18": {
	"ref_id": "b18",
	"title": "449 ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns Yuhuan Xiu, Man Lan and",
	"authors": [],
	"year": null,
	"venue": "UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns Ankit Vadehra",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns Ankit Vadehra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns Yuhuan Xiu, Man Lan and Yuanbin Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453",
	"links": null
	},
	"BIBREF19": {
	"ref_id": "b19",
	"title": "Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language Vijayasaradhi Indurthi and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language Vijayasaradhi Indurthi and Subba Reddy Oota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457",
	"links": null
	},
	"BIBREF20": {
	"ref_id": "b20",
	"title": "Detecting Stance towards Rumours with Topic Independent Features Hareesh Bahuleyan and Olga Vechtomova",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "8",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Fea- tures Hareesh Bahuleyan and Olga Vechtomova. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .461",
	"links": null
	},
	"BIBREF21": {
	"ref_id": "b21",
	"title": "IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification Yi-Chin Chen",
	"authors": [
	{
	"first": "Yang",
	"middle": [],
	"last": "Zhao",
	"suffix": ""
	},
	{
	"first": "Hung-Yu",
	"middle": [],
	"last": "Liu",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Kao",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verifica- tion Yi-Chin Chen, Zhao-Yang Liu and Hung-Yu Kao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465",
	"links": null
	},
	"BIBREF22": {
	"ref_id": "b22",
	"title": "2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.",
	"links": null
	},
	"BIBREF24": {
	"ref_id": "b24",
	"title": "Sequential Approach to Rumour Stance Classification with Branch-LSTM Elena Kochkina, Maria Liakata and Isabelle Augenstein",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "8",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch- LSTM Elena Kochkina, Maria Liakata and Isabelle Augenstein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475",
	"links": null
	},
	"BIBREF25": {
	"ref_id": "b25",
	"title": "Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules",
	"authors": [
	{
	"first": "Marianela",
	"middle": [
	"Garc\u00eda"
	],
	"last": "Lozano",
	"suffix": ""
	},
	{
	"first": "Hanna",
	"middle": [],
	"last": "Lilja",
	"suffix": ""
	},
	{
	"first": "Edward",
	"middle": [],
	"last": "Tj\u00f6rnhammar",
	"suffix": ""
	},
	{
	"first": "Maja",
	"middle": [
	". . . . ."
	],
	"last": "Karasalo",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules Marianela Garc\u00eda Lozano, Hanna Lilja, Edward Tj\u00f6rnhammar and Maja Karasalo . . . . . . . . . . . . 481",
	"links": null
	},
	"BIBREF26": {
	"ref_id": "b26",
	"title": "Rumour Detection and Classification using Cascading Heuristics Ankit Srivastava",
	"authors": [
	{
	"first": "Georg",
	"middle": [],
	"last": "Rehm",
	"suffix": ""
	},
	{
	"first": "Julian Moreno",
	"middle": [
	". ."
	],
	"last": "Schneider",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "8",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics Ankit Srivastava, Georg Rehm and Julian Moreno Schneider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486",
	"links": null
	},
	"BIBREF27": {
	"ref_id": "b27",
	"title": "Rumour Evaluation Using Effective Features and Supervised Ensemble Models Feixiang",
	"authors": [
	{
	"first": "Man",
	"middle": [],
	"last": "Wang",
	"suffix": ""
	},
	{
	"first": "Yuanbin",
	"middle": [],
	"last": "Lan",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Wu",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "8",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models Feixiang Wang, Man Lan and Yuanbin Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491",
	"links": null
	},
	"BIBREF28": {
	"ref_id": "b28",
	"title": "A Supervised Approach for Rumour Evaluation Vikram Singh, Sunny Narayan, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "8",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation Vikram Singh, Sunny Narayan, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya . . . . . 497",
	"links": null
	},
	"BIBREF29": {
	"ref_id": "b29",
	"title": "Sentiment Analysis in Twitter Sara Rosenthal, Noura Farra and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SemEval-2017 Task 4: Sentiment Analysis in Twitter Sara Rosenthal, Noura Farra and Preslav Nakov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502",
	"links": null
	},
	"BIBREF30": {
	"ref_id": "b30",
	"title": "Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Keith Cortis",
	"authors": [
	{
	"first": "Andr\u00e9",
	"middle": [],
	"last": "Freitas",
	"suffix": ""
	},
	{
	"first": "Tobias",
	"middle": [],
	"last": "Daudert",
	"suffix": ""
	},
	{
	"first": "Manuela",
	"middle": [],
	"last": "Huerlimann",
	"suffix": ""
	},
	{
	"first": "Manel",
	"middle": [],
	"last": "Zarrouk",
	"suffix": ""
	},
	{
	"first": "Siegfried",
	"middle": [],
	"last": "Handschuh",
	"suffix": ""
	},
	{
	"first": "Brian",
	"middle": [
	"Davis"
	],
	"last": "",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Keith Cortis, Andr\u00e9 Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Hand- schuh and Brian Davis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519",
	"links": null
	},
	"BIBREF31": {
	"ref_id": "b31",
	"title": "Abstract Meaning Representation Parsing and Generation Jonathan May and",
	"authors": [
	{
	"first": ".",
	"middle": [
	". ."
	],
	"last": "Jay Priyadarshi",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "10",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation Jonathan May and Jay Priyadarshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536 xii SemEval 2017 Task 10: ScienceIE -Extracting Keyphrases and Relations from Scientific Publications Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman and Andrew McCallum546",
	"links": null
	},
	"BIBREF32": {
	"ref_id": "b32",
	"title": "End-User Development using Natural Language Juliano Sales, Siegfried Handschuh and Andr\u00e9 Freitas",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "11",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SemEval-2017 Task 11: End-User Development using Natural Language Juliano Sales, Siegfried Handschuh and Andr\u00e9 Freitas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556",
	"links": null
	},
	"BIBREF34": {
	"ref_id": "b34",
	"title": "Twitter Sentiment Analysis with CNNs and LSTMs Mathieu Cliche",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs Mathieu Cliche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573",
	"links": null
	},
	"BIBREF35": {
	"ref_id": "b35",
	"title": "Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines",
	"links": null
	},
	"BIBREF37": {
	"ref_id": "b37",
	"title": "Transition-based language generation from AMR. Gerasimos Lampouras and Andreas Vlachos",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "9",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR. Gerasimos Lampouras and Andreas Vlachos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586",
	"links": null
	},
	"BIBREF38": {
	"ref_id": "b38",
	"title": "ScienceIE): semi-supervised end-to-end entity and relation extraction Waleed Ammar",
	"authors": [
	{
	"first": "Matthew",
	"middle": [],
	"last": "Peters",
	"suffix": ""
	},
	{
	"first": "Chandra",
	"middle": [],
	"last": "Bhagavatula",
	"suffix": ""
	},
	{
	"first": "Russell",
	"middle": [
	". . . . . . . . . . . . . ."
	],
	"last": "Power",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "The AI2 system at SemEval-2017 Task",
	"volume": "10",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction Waleed Ammar, Matthew Peters, Chandra Bhagavatula and Russell Power . . . . . . . . . . . . . . . . . . 592",
	"links": null
	},
	"BIBREF39": {
	"ref_id": "b39",
	"title": "Neural Architecture for Temporal Information Extraction from Clinical Narratives Julien Tourille",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives Julien Tourille, Olivier Ferret, Xavier Tannier and Aur\u00e9lie N\u00e9v\u00e9ol . . . . . . . . . . . . . . . . . . . . . . . . . . 597",
	"links": null
	},
	"BIBREF40": {
	"ref_id": "b40",
	"title": "Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model Ramy",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El-Khoury, Ahmad",
	"links": null
	},
	"BIBREF42": {
	"ref_id": "b42",
	"title": "Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis Edilson Anselmo Corr\u00eaa J\u00fanior, Vanessa Queiroz Marinho and Leandro Borges dos Santos",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis Edilson Anselmo Corr\u00eaa J\u00fanior, Vanessa Queiroz Marinho and Leandro Borges dos Santos . . . 611",
	"links": null
	},
	"BIBREF43": {
	"ref_id": "b43",
	"title": "2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter Tzu-Hsuan Yang",
	"authors": [
	{
	"first": "",
	"middle": [],
	"last": "Tzu-Hsuan",
	"suffix": ""
	},
	{
	"first": "Chia-Ping",
	"middle": [],
	"last": "Tseng",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". ."
	],
	"last": "Chen",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter Tzu-Hsuan Yang, Tzu-Hsuan Tseng and Chia-Ping Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616",
	"links": null
	},
	"BIBREF44": {
	"ref_id": "b44",
	"title": "Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings Yichun Yin",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings Yichun Yin, Yangqiu Song and Ming Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621",
	"links": null
	},
	"BIBREF45": {
	"ref_id": "b45",
	"title": "Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification Raj Kumar Gupta and Yinping Yang",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification Raj Kumar Gupta and Yinping Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626",
	"links": null
	},
	"BIBREF46": {
	"ref_id": "b46",
	"title": "SINAI at SemEval-2017 Task 4: User based classification Salud",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SINAI at SemEval-2017 Task 4: User based classification Salud Mar\u00eda Jim\u00e9nez-Zafra, Arturo Montejo-R\u00e1ez, Maite Martin and L. Alfonso Urena Lopez 634",
	"links": null
	},
	"BIBREF47": {
	"ref_id": "b47",
	"title": "HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors Abeed Sarker and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors Abeed Sarker and Graciela Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640",
	"links": null
	},
	"BIBREF48": {
	"ref_id": "b48",
	"title": "Experiments for Target oriented Sentiment Analysis in Twitter Enkhzol Dovdon and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter Enkhzol Dovdon and Jos\u00e9 Saias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644",
	"links": null
	},
	"BIBREF49": {
	"ref_id": "b49",
	"title": "Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification Raphael Troncy",
	"authors": [
	{
	"first": "Enrico",
	"middle": [],
	"last": "Palumbo",
	"suffix": ""
	},
	{
	"first": "Efstratios",
	"middle": [],
	"last": "Sygkounas",
	"suffix": ""
	},
	{
	"first": "Giuseppe",
	"middle": [
	". . . . . . . . . . . . ."
	],
	"last": "Rizzo",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Clas- sification Raphael Troncy, Enrico Palumbo, Efstratios Sygkounas and Giuseppe Rizzo. . . . . . . . . . . . . . . . .648",
	"links": null
	},
	"BIBREF50": {
	"ref_id": "b50",
	"title": "Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter Alon Rozental and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter Alon Rozental and Daniel Fleischer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653",
	"links": null
	},
	"BIBREF51": {
	"ref_id": "b51",
	"title": "Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier Naveen Kumar Laskari and Suresh Kumar Sanampudi",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classi- fier Naveen Kumar Laskari and Suresh Kumar Sanampudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659",
	"links": null
	},
	"BIBREF52": {
	"ref_id": "b52",
	"title": "Sentiment Classification of Arabic Tweets Hala Mulki, Hatem Haddad, Mourad Gridach and Ismail Babaoglu",
	"authors": [
	{
	"first": "",
	"middle": [],
	"last": "Tw-Star",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets Hala Mulki, Hatem Haddad, Mourad Gridach and Ismail Babaoglu . . . . . . . . . . . . . . . . . . . . . . . . . 664",
	"links": null
	},
	"BIBREF53": {
	"ref_id": "b53",
	"title": "OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields Chukwuyem Onyibe and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields Chukwuyem Onyibe and Nizar Habash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670",
	"links": null
	},
	"BIBREF54": {
	"ref_id": "b54",
	"title": "Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis",
	"authors": [
	{
	"first": "Pinelopi",
	"middle": [],
	"last": "Papalampidi",
	"suffix": ""
	},
	{
	"first": "Elias",
	"middle": [],
	"last": "Iosif",
	"suffix": ""
	},
	{
	"first": "Nikolaos",
	"middle": [],
	"last": "Malandrakis",
	"suffix": ""
	},
	{
	"first": "Elisavet",
	"middle": [],
	"last": "Palogiannidi",
	"suffix": ""
	},
	{
	"first": "Haris",
	"middle": [],
	"last": "Papageorgiou",
	"suffix": ""
	},
	{
	"first": "Shrikanth",
	"middle": [],
	"last": "Narayanan",
	"suffix": ""
	},
	{
	"first": "Alexandros",
	"middle": [],
	"last": "Potamianos",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Nikolaos Malandrakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan and Alexandros Potami- anos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675",
	"links": null
	},
	"BIBREF55": {
	"ref_id": "b55",
	"title": "NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture Nikolay Karpov",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture Nikolay Karpov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683",
	"links": null
	},
	"BIBREF56": {
	"ref_id": "b56",
	"title": "MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification Jingjing",
	"authors": [
	{
	"first": "Yan",
	"middle": [],
	"last": "Zhao",
	"suffix": ""
	},
	{
	"first": "Bing",
	"middle": [],
	"last": "Yang",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Xu",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Clas- sification Jingjing Zhao, Yan Yang and Bing Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689",
	"links": null
	},
	"BIBREF57": {
	"ref_id": "b57",
	"title": "Sentiment Analysis in Twitter Based on a Rich Set of Features Mohammed Jabreel and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features Mohammed Jabreel and Antonio Moreno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694",
	"links": null
	},
	"BIBREF58": {
	"ref_id": "b58",
	"title": "Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification Hussam Hamdan",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classifi- cation Hussam Hamdan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700",
	"links": null
	},
	"BIBREF59": {
	"ref_id": "b59",
	"title": "DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis Symeon Symeonidis, Dimitrios Effrosynidis",
	"authors": [
	{
	"first": "John",
	"middle": [],
	"last": "Kordonis",
	"suffix": ""
	},
	{
	"first": "Avi",
	"middle": [
	". . . . ."
	],
	"last": "Arampatzis",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis and Avi Arampatzis . . . . . . . . . . . . 704",
	"links": null
	},
	"BIBREF60": {
	"ref_id": "b60",
	"title": "Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier Angel",
	"authors": [
	{
	"first": "S",
	"middle": [],
	"last": "Deborah",
	"suffix": ""
	},
	{
	"first": "Milton",
	"middle": [],
	"last": "Rajendram",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". ."
	],
	"last": "Mirnalinee",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Pro- cess Classifier Angel Deborah S, S Milton Rajendram and T T Mirnalinee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709",
	"links": null
	},
	"BIBREF61": {
	"ref_id": "b61",
	"title": "YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter Ming Wang",
	"authors": [
	{
	"first": "Biao",
	"middle": [],
	"last": "Chu",
	"suffix": ""
	},
	{
	"first": "Qingxun",
	"middle": [],
	"last": "Liu",
	"suffix": ""
	},
	{
	"first": "Xiaobing",
	"middle": [],
	"last": "Zhou",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter Ming Wang, Biao Chu, Qingxun Liu and Xiaobing Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713",
	"links": null
	},
	"BIBREF62": {
	"ref_id": "b62",
	"title": "LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification Amal Htait",
	"authors": [
	{
	"first": "S\u00e9bastien",
	"middle": [],
	"last": "Fournier",
	"suffix": ""
	},
	{
	"first": "Patrice",
	"middle": [],
	"last": "Bellot",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification Amal Htait, S\u00e9bastien Fournier and Patrice Bellot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718",
	"links": null
	},
	"BIBREF63": {
	"ref_id": "b63",
	"title": "723 xiv XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in",
	"authors": [
	{
	"first": "Ferran",
	"middle": [],
	"last": "Pla",
	"suffix": ""
	},
	{
	"first": "Llu\u00eds-F",
	"middle": [],
	"last": "Hurtado",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "Sentiment Analysis using Deep Learning Jos\u00e9-\u00c1ngel Gonz\u00e1lez",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning Jos\u00e9-\u00c1ngel Gonz\u00e1lez, Ferran Pla and Llu\u00eds-F. Hurtado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 xiv XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter Yazhou Hao, YangYang Lan, Yufei Li and Chen Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 728",
	"links": null
	},
	"BIBREF64": {
	"ref_id": "b64",
	"title": "Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention Joosung Yoon, Kigon Lyu and",
	"authors": [
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Hyeoncheol Kim",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention Joosung Yoon, Kigon Lyu and Hyeoncheol Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732",
	"links": null
	},
	"BIBREF65": {
	"ref_id": "b65",
	"title": "EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification wang maoquan",
	"authors": [
	{
	"first": "Chen",
	"middle": [],
	"last": "Shiyun",
	"suffix": ""
	},
	{
	"first": "Xie",
	"middle": [],
	"last": "Zhao Lu",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification wang maoquan, Chen Shiyun, Xie yufei and Zhao lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737",
	"links": null
	},
	"BIBREF66": {
	"ref_id": "b66",
	"title": "Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang and",
	"authors": [
	{
	"first": "Sameena",
	"middle": [
	". . . . . . . . . . ."
	],
	"last": "Shah",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang and Sameena Shah . . . . . . . . . . . . . . . 741",
	"links": null
	},
	"BIBREF67": {
	"ref_id": "b67",
	"title": "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis Christos Baziotis, Nikos Pelekis and Christos Doulkeridis",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis Christos Baziotis, Nikos Pelekis and Christos Doulkeridis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747",
	"links": null
	},
	"BIBREF68": {
	"ref_id": "b68",
	"title": "TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification Georgios Balikas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755",
	"links": null
	},
	"BIBREF69": {
	"ref_id": "b69",
	"title": "LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification Mickael Rouvier",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification Mickael Rouvier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760",
	"links": null
	},
	"BIBREF70": {
	"ref_id": "b70",
	"title": "Sentiment Classification Using a Convolutional Neural Network with Distant Supervision Simon M\u00fcller",
	"authors": [
	{
	"first": "Tobias",
	"middle": [],
	"last": "Huonder",
	"suffix": ""
	},
	{
	"first": "Jan",
	"middle": [],
	"last": "Deriu",
	"suffix": ""
	},
	{
	"first": "Mark",
	"middle": [
	". ."
	],
	"last": "Cieliebak",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision Simon M\u00fcller, Tobias Huonder, Jan Deriu and Mark Cieliebak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766",
	"links": null
	},
	"BIBREF71": {
	"ref_id": "b71",
	"title": "A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis Sabino",
	"authors": [
	{
	"first": "Mario",
	"middle": [],
	"last": "Miranda-Jim\u00e9nez",
	"suffix": ""
	},
	{
	"first": "Eric",
	"middle": [
	"Sadit"
	],
	"last": "Graff",
	"suffix": ""
	},
	{
	"first": "Daniela",
	"middle": [],
	"last": "Tellez",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". . . . ."
	],
	"last": "Moctezuma",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "4",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis Sabino Miranda-Jim\u00e9nez, Mario Graff, Eric Sadit Tellez and Daniela Moctezuma . . . . . . . . . . . . 771",
	"links": null
	},
	"BIBREF72": {
	"ref_id": "b72",
	"title": "BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches Deger Ayata",
	"authors": [
	{
	"first": "Murat",
	"middle": [],
	"last": "Saraclar And Arzucan Ozgur",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Mem- ory RNN Approaches Deger Ayata, Murat Saraclar and Arzucan Ozgur. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .777",
	"links": null
	},
	"BIBREF73": {
	"ref_id": "b73",
	"title": "TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter David Lozi\u0107",
	"authors": [
	{
	"first": "Doria",
	"middle": [],
	"last": "\u0160ari\u0107",
	"suffix": ""
	},
	{
	"first": "Ivan",
	"middle": [],
	"last": "Toki\u0107",
	"suffix": ""
	},
	{
	"first": "Zoran",
	"middle": [],
	"last": "Medi\u0107",
	"suffix": ""
	},
	{
	"first": "Jan",
	"middle": [],
	"last": "\u0160najder",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter David Lozi\u0107, Doria \u0160ari\u0107, Ivan Toki\u0107, Zoran Medi\u0107 and Jan \u0160najder . . . . . . . . . . . . . . . . . . . . . . . . 784",
	"links": null
	},
	"BIBREF74": {
	"ref_id": "b74",
	"title": "YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification Haowei Zhang",
	"authors": [
	{
	"first": "Jin",
	"middle": [],
	"last": "Wang",
	"suffix": ""
	},
	{
	"first": "Jixian",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	},
	{
	"first": "Xuejie",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classi- fication Haowei Zhang, Jin Wang, Jixian Zhang and Xuejie Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796",
	"links": null
	},
	"BIBREF75": {
	"ref_id": "b75",
	"title": "TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis Amit Ajit Deshmane and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis Amit Ajit Deshmane and Jasper Friedrichs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802",
	"links": null
	},
	"BIBREF76": {
	"ref_id": "b76",
	"title": "807 xv ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification Yunxiao Zhou, Man Lan and",
	"authors": [
	{
	"first": "Iv\u00e1n",
	"middle": [],
	"last": "Castro",
	"suffix": ""
	},
	{
	"first": "Claudia",
	"middle": [],
	"last": "Mart\u00ednez",
	"suffix": ""
	},
	{
	"first": "Sebasti\u00e1n",
	"middle": [],
	"last": "Oliva",
	"suffix": ""
	},
	{
	"first": "Yoan",
	"middle": [
	". . . . . ."
	],
	"last": "Guti\u00e9rrez",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter Jos\u00e9 Abreu",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter Jos\u00e9 Abreu, Iv\u00e1n Castro, Claudia Mart\u00ednez, Sebasti\u00e1n Oliva and Yoan Guti\u00e9rrez . . . . . . . . . . . . . 807 xv ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification Yunxiao Zhou, Man Lan and Yuanbin Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812",
	"links": null
	},
	"BIBREF77": {
	"ref_id": "b77",
	"title": "Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines Youness Mansar",
	"authors": [
	{
	"first": "Lorenzo",
	"middle": [],
	"last": "Gatti",
	"suffix": ""
	},
	{
	"first": "Sira",
	"middle": [],
	"last": "Ferradans",
	"suffix": ""
	},
	{
	"first": "Marco",
	"middle": [],
	"last": "Guerini",
	"suffix": ""
	},
	{
	"first": "Jacopo",
	"middle": [
	". . . ."
	],
	"last": "Staiano",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini and Jacopo Staiano . . . . . . . . . . 817",
	"links": null
	},
	"BIBREF78": {
	"ref_id": "b78",
	"title": "SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model Angel",
	"authors": [
	{
	"first": "S",
	"middle": [],
	"last": "Deborah",
	"suffix": ""
	},
	{
	"first": "Milton",
	"middle": [],
	"last": "Rajendram",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". ."
	],
	"last": "Mirnalinee",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaus- sian Process Regression Model Angel Deborah S, S Milton Rajendram and T T Mirnalinee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823",
	"links": null
	},
	"BIBREF79": {
	"ref_id": "b79",
	"title": "IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Zarmeen Nasim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827",
	"links": null
	},
	"BIBREF80": {
	"ref_id": "b80",
	"title": "HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods Tobias Cabanski",
	"authors": [
	{
	"first": "Julia",
	"middle": [],
	"last": "Romberg",
	"suffix": ""
	},
	{
	"first": "Stefan",
	"middle": [
	"Conrad"
	],
	"last": "",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods Tobias Cabanski, Julia Romberg and Stefan Conrad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832",
	"links": null
	},
	"BIBREF81": {
	"ref_id": "b81",
	"title": "INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines Tiago Zini",
	"authors": [
	{
	"first": "Karin",
	"middle": [],
	"last": "Becker",
	"suffix": ""
	},
	{
	"first": "Marcelo",
	"middle": [],
	"last": "Dias",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines Tiago Zini, Karin Becker and Marcelo Dias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837",
	"links": null
	},
	"BIBREF82": {
	"ref_id": "b82",
	"title": "HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks Lidia Pivovarova",
	"authors": [
	{
	"first": "Lloren\u00e7",
	"middle": [],
	"last": "Escoter",
	"suffix": ""
	},
	{
	"first": "Arto",
	"middle": [],
	"last": "Klami",
	"suffix": ""
	},
	{
	"first": "Roman",
	"middle": [],
	"last": "Yangarber",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks Lidia Pivovarova, Lloren\u00e7 Escoter, Arto Klami and Roman Yangarber . . . . . . . . . . . . . . . . . . . . . . 842",
	"links": null
	},
	"BIBREF83": {
	"ref_id": "b83",
	"title": "NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Chung-Chi Chen, Hen-Hsen Huang and",
	"authors": [
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Hsin-Hsi Chen",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Chung-Chi Chen, Hen-Hsen Huang and Hsin-Hsi Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 847",
	"links": null
	},
	"BIBREF84": {
	"ref_id": "b84",
	"title": "2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang and",
	"authors": [
	{
	"first": "Xiaomo",
	"middle": [
	". . . . . . . . . . ."
	],
	"last": "Liu",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang and Xiaomo Liu . . . . . . . . . . . . . . . 852",
	"links": null
	},
	"BIBREF85": {
	"ref_id": "b85",
	"title": "SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets Narges Tabari",
	"authors": [
	{
	"first": "Armin",
	"middle": [],
	"last": "Seyeditabari",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Wlodek Zadrozny",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets Narges Tabari, Armin Seyeditabari and Wlodek Zadrozny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857",
	"links": null
	},
	"BIBREF86": {
	"ref_id": "b86",
	"title": "DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis and",
	"authors": [
	{
	"first": "Avi",
	"middle": [
	". . . . ."
	],
	"last": "Arampatzis",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis and Avi Arampatzis . . . . . . . . . . . . 861",
	"links": null
	},
	"BIBREF87": {
	"ref_id": "b87",
	"title": "Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news Leon Rotim, Martin Tutek and",
	"authors": [
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "\u0160najder",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "5",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news Leon Rotim, Martin Tutek and Jan \u0160najder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 866",
	"links": null
	},
	"BIBREF88": {
	"ref_id": "b88",
	"title": "UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation Vineet John and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation Vineet John and Olga Vechtomova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872",
	"links": null
	},
	"BIBREF89": {
	"ref_id": "b89",
	"title": "883 xvi ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain Mengxiao Jiang",
	"authors": [
	{
	"first": "Suraj",
	"middle": [],
	"last": "Maharjan",
	"suffix": ""
	},
	{
	"first": "Thamar",
	"middle": [],
	"last": "Solorio",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "Sentiment Analysis on Financial Data Using Neural Networks Sudipta Kar",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks Sudipta Kar, Suraj Maharjan and Thamar Solorio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877 COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Head- lines Kim Schouten, Flavius Frasincar and Franciska de Jong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883 xvi ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine- Grained Sentiment Analysis in Financial Domain Mengxiao Jiang, Man Lan and Yuanbin Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 888",
	"links": null
	},
	"BIBREF90": {
	"ref_id": "b90",
	"title": "894 IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis Deepanway Ghosal, Shobhit Bhatnagar, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya 899 FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings Pedro Saleiro",
	"authors": [
	{
	"first": "Abhishek",
	"middle": [],
	"last": "Sethi",
	"suffix": ""
	},
	{
	"first": "Shad",
	"middle": [],
	"last": "Md",
	"suffix": ""
	},
	{
	"first": "Asif",
	"middle": [],
	"last": "Akhtar",
	"suffix": ""
	},
	{
	"first": "Chris",
	"middle": [],
	"last": "Ekbal",
	"suffix": ""
	},
	{
	"first": "Pushpak",
	"middle": [],
	"last": "Biemann",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". ."
	],
	"last": "Bhattacharyya",
	"suffix": ""
	},
	{
	"first": "Carlos",
	"middle": [],
	"last": "Rodrigues",
	"suffix": ""
	},
	{
	"first": "Eug\u00e9nio",
	"middle": [],
	"last": "Soares",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". . . . . . . . . ."
	],
	"last": "Oliveira",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text Abhishek Kumar",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann and Pushpak Bhat- tacharyya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894 IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis Deepanway Ghosal, Shobhit Bhatnagar, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya 899 FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Em- beddings Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares and Eug\u00e9nio Oliveira . . . . . . . . . . . . . . 904",
	"links": null
	},
	"BIBREF91": {
	"ref_id": "b91",
	"title": "Building Scientific Concept Fixing Patterns for Improving CAMR Khoa Nguyen and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improv- ing CAMR Khoa Nguyen and Dang Nguyen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909",
	"links": null
	},
	"BIBREF92": {
	"ref_id": "b92",
	"title": "Neural AMR Parsing with Pointer-Augmented Attention Jan Buys and",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "9",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention Jan Buys and Phil Blunsom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914",
	"links": null
	},
	"BIBREF93": {
	"ref_id": "b93",
	"title": "Deep sentence generation based on a sequence of graph transducers Simon Mille",
	"authors": [
	{
	"first": "Roberto",
	"middle": [],
	"last": "Carlini",
	"suffix": ""
	},
	{
	"first": "Alicia",
	"middle": [],
	"last": "Burga",
	"suffix": ""
	},
	{
	"first": "Leo",
	"middle": [],
	"last": "Wanner",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "9",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers Simon Mille, Roberto Carlini, Alicia Burga and Leo Wanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 920",
	"links": null
	},
	"BIBREF94": {
	"ref_id": "b94",
	"title": "Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation Normunds Gruzitis, Didzis Gosko and Guntis Barzdins",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation Normunds Gruzitis, Didzis Gosko and Guntis Barzdins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924",
	"links": null
	},
	"BIBREF95": {
	"ref_id": "b95",
	"title": "929 PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge Liang Wang and Sujian Li",
	"authors": [
	{
	"first": "Johan",
	"middle": [],
	"last": "Van Noord",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Bos",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing Rik",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing Rik van Noord and Johan Bos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929 PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowl- edge Liang Wang and Sujian Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934",
	"links": null
	},
	"BIBREF96": {
	"ref_id": "b96",
	"title": "NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields Erwin Marsi",
	"authors": [
	{
	"first": "Cristina",
	"middle": [],
	"last": "Utpal Kumar Sikdar",
	"suffix": ""
	},
	{
	"first": "Biswanath",
	"middle": [],
	"last": "Marco",
	"suffix": ""
	},
	{
	"first": "Rune",
	"middle": [],
	"last": "Barik",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". . . ."
	],
	"last": "Saetre",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik and Rune Saetre . . . . . . . . . 938",
	"links": null
	},
	"BIBREF97": {
	"ref_id": "b97",
	"title": "Ensemble of nEural Learners for kEyphrase ClassificaTION Steffen Eger, Erik-L\u00e2n Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha and Iryna Gurevych",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "10",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION Steffen Eger, Erik-L\u00e2n Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha and Iryna Gurevych . . . . . . . 942",
	"links": null
	},
	"BIBREF98": {
	"ref_id": "b98",
	"title": "Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network Isabel Segura-Bedmar, Crist\u00f3bal Col\u00f3n-Ruiz and Paloma Mart\u00ednez . . . . . . . . . . . . . . . . . . . . . . . . . 947",
	"links": null
	},
	"BIBREF99": {
	"ref_id": "b99",
	"title": "Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications Sijia Liu, Feichen Shen, Vipin Chaudhary and Hongfang Liu",
	"authors": [
	{
	"first": "Hao",
	"middle": [],
	"last": "Lee",
	"suffix": ""
	},
	{
	"first": "Kuei-Ching",
	"middle": [],
	"last": "Lee",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Yuen-Hsien Tseng",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Pub- lications Using Multiple Conditional Random Fields Lung-Hao Lee, Kuei-Ching Lee and Yuen-Hsien Tseng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 xvii MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications Sijia Liu, Feichen Shen, Vipin Chaudhary and Hongfang Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956",
	"links": null
	},
	"BIBREF100": {
	"ref_id": "b100",
	"title": "Sequence Classification with the CODE Annotator Roman Kern, Stefan Falk and Andi Rexha",
	"authors": [
	{
	"first": "",
	"middle": [],
	"last": "Know-Center",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator Roman Kern, Stefan Falk and Andi Rexha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961",
	"links": null
	},
	"BIBREF101": {
	"ref_id": "b101",
	"title": "Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents Biswanath Barik and Erwin Marsi",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents Biswanath Barik and Erwin Marsi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965",
	"links": null
	},
	"BIBREF102": {
	"ref_id": "b102",
	"title": "Relation Classification between keyphrases via Convolutional Neural Network V\u00edctor Su\u00e1rez-Paniagua, Isabel Segura-Bedmar and Paloma Mart\u00ednez",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network V\u00edctor Su\u00e1rez-Paniagua, Isabel Segura-Bedmar and Paloma Mart\u00ednez . . . . . . . . . . . . . . . . . . . . . . . 969",
	"links": null
	},
	"BIBREF103": {
	"ref_id": "b103",
	"title": "Keyphrase Extraction and Classification as Joint Sequence Labeling Animesh Prasad and Min-Yen",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence La- beling Animesh Prasad and Min-Yen Kan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973",
	"links": null
	},
	"BIBREF104": {
	"ref_id": "b104",
	"title": "Relation Extraction with Convolutional Neural Networks Ji Young Lee, Franck Dernoncourt and Peter Szolovits",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks Ji Young Lee, Franck Dernoncourt and Peter Szolovits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978",
	"links": null
	},
	"BIBREF105": {
	"ref_id": "b105",
	"title": "Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers Tomoki Tsujimura",
	"authors": [
	{
	"first": "Makoto",
	"middle": [],
	"last": "Miwa",
	"suffix": ""
	},
	{
	"first": "Yutaka",
	"middle": [],
	"last": "Sasaki",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers Tomoki Tsujimura, Makoto Miwa and Yutaka Sasaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985",
	"links": null
	},
	"BIBREF106": {
	"ref_id": "b106",
	"title": "990 LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Partof-Speech Tag Sequences to Train a Sequence Labeling",
	"authors": [],
	"year": null,
	"venue": "SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation G\u00e1bor Berend",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Uti- lizing Sparse Coding for Feature Generation G\u00e1bor Berend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 990 LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part- of-Speech Tag Sequences to Train a Sequence Labeling Model Simon David Hernandez, Davide Buscaldi and Thierry Charnois . . . . . . . . . . . . . . . . . . . . . . . . . . . 995",
	"links": null
	},
	"BIBREF107": {
	"ref_id": "b107",
	"title": "Action Ranking and Type Matching for End-User Development Marek Kubis, Pawe\u0142 Sk\u00f3rzewski and Tomasz Zi\u0119tkiewicz",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development Marek Kubis, Pawe\u0142 Sk\u00f3rzewski and Tomasz Zi\u0119tkiewicz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000",
	"links": null
	},
	"BIBREF108": {
	"ref_id": "b108",
	"title": "System for temporal information extraction from clinical notes Sarath P",
	"authors": [
	{
	"first": "R",
	"middle": [],
	"last": "",
	"suffix": ""
	},
	{
	"first": "Manikandan",
	"middle": [
	"R"
	],
	"last": "",
	"suffix": ""
	},
	{
	"first": "Yoshiki",
	"middle": [],
	"last": "Niwa",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "12",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes Sarath P R, Manikandan R and Yoshiki Niwa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005",
	"links": null
	},
	"BIBREF109": {
	"ref_id": "b109",
	"title": "Detection and classification of temporal events in clinical data with domain adaptation",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang and Hsin-Hsi Chen . . . . . . . . . . 1010",
	"links": null
	},
	"BIBREF110": {
	"ref_id": "b110",
	"title": "Clinical temporal information ex-traction with a Hybrid Model Yu Long",
	"authors": [
	{
	"first": "Zhijing",
	"middle": [],
	"last": "Li",
	"suffix": ""
	},
	{
	"first": "Xuan",
	"middle": [],
	"last": "Wang",
	"suffix": ""
	},
	{
	"first": "Chen",
	"middle": [],
	"last": "Li",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model Yu Long, Zhijing Li, Xuan Wang and Chen Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014",
	"links": null
	},
	"BIBREF111": {
	"ref_id": "b111",
	"title": "Extraction and classification of temporal expressions and events",
	"authors": [
	{
	"first": "Andre",
	"middle": [],
	"last": "Lamurias",
	"suffix": ""
	},
	{
	"first": "Diana",
	"middle": [],
	"last": "Sousa",
	"suffix": ""
	},
	{
	"first": "Sofia",
	"middle": [],
	"last": "Pereira",
	"suffix": ""
	},
	{
	"first": "Luka",
	"middle": [],
	"last": "Clarke",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [
	". . . ."
	],
	"last": "Francisco M Couto",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events Andre Lamurias, Diana Sousa, Sofia Pereira, Luka Clarke and Francisco M Couto . . . . . . . . . . 1019",
	"links": null
	},
	"BIBREF112": {
	"ref_id": "b112",
	"title": "A Framework for Cross-Domain Clinical Temporal Information Extraction Sean MacAvaney",
	"authors": [
	{
	"first": "Arman",
	"middle": [],
	"last": "Cohan",
	"suffix": ""
	},
	{
	"first": ".",
	"middle": [
	"."
	],
	"last": "Nazli Goharian",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Ex- traction Sean MacAvaney, Arman Cohan and Nazli Goharian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1024",
	"links": null
	},
	"BIBREF113": {
	"ref_id": "b113",
	"title": ":30 HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment Behrang QasemiZadeh and Laura Kallmeyer 16:30-17:30 Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledgebased Methods to Measure Semantic Word Similarity Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour and Aryan Vahid pour 16:30-17:30 Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia Claudio Delli Bovi and Alessandro Raganato 16:30-17:30 Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity",
	"authors": [
	{
	"first": "Marie-Francine",
	"middle": [
	". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ; Wenpeng"
	],
	"last": "Moens",
	"suffix": ""
	},
	{
	"first": "Yuteng",
	"middle": [],
	"last": "Lu",
	"suffix": ""
	},
	{
	"first": "Ping",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	},
	{
	"first": "Shumin",
	"middle": [],
	"last": "Jian",
	"suffix": ""
	},
	{
	"first": "Heyan",
	"middle": [],
	"last": "Shi",
	"suffix": ""
	},
	{
	"first": "P",
	"middle": [],
	"last": "Huang ; Daniele",
	"suffix": ""
	},
	{
	"first": "Antonio ; Maoquan",
	"middle": [],
	"last": "Radicioni",
	"suffix": ""
	},
	{
	"first": "Jing",
	"middle": [],
	"last": "Wang",
	"suffix": ""
	},
	{
	"first": "Jian",
	"middle": [],
	"last": "Ma",
	"suffix": ""
	},
	{
	"first": "Zhao",
	"middle": [],
	"last": "Jiang",
	"suffix": ""
	},
	{
	"first": "; Hui",
	"middle": [],
	"last": "Lu",
	"suffix": ""
	},
	{
	"first": "Xin",
	"middle": [],
	"last": "Wang",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "30 SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering Jan Milan Deriu and Mark Cieliebak",
	"volume": "12",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clini- cal Records Artuur Leeuwenberg and Marie-Francine Moens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1030 16:30-17:30 Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity Josu\u00e9 Melka and Gilles Bernard 16:30-17:30 QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi and Heyan Huang 16:30-17:30 RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list Sergio Jimenez, George Due\u00f1as, Lorena Gaitan and Jorge Segura 16:30-17:30 MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach Enrico Mensa, Daniele P. Radicioni and Antonio Lieto 16:30-17:30 HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment Behrang QasemiZadeh and Laura Kallmeyer 16:30-17:30 Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge- based Methods to Measure Semantic Word Similarity Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hos- seini pour and Aryan Vahid pour 16:30-17:30 Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representa- tions from a Semantically Enriched Wikipedia Claudio Delli Bovi and Alessandro Raganato 16:30-17:30 Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity R\u01cezvan-Gabriel Rotari, Ionut Hulub, Stefan Oprea, Mihaela Plamada-Onofrei, Alina Beatrice Lorent, Raluca Preisler, Adrian Iftene and Diana Trandabat 16:30-17:30 TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers Mohammed R. H. Qwaider, Abed Alhakim Freihat and Fausto Giunchiglia 16:30-17:30 UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic Yassine El Adlouni, Imane Lahbari, Horacio Rodriguez, Mohammed Meknassi, Said Ouatik El Alaoui and Noureddine Ennahnahi 16:30-17:30 Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li and Ming Zhou 16:30-17:30 MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks Miguel J. Rodrigues and Francisco M Couto xxv 3 August 2017 (continued) 16:30-17:30 EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang and Zhao Lu 16:30-17:30 FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto and Ludovica Pannitto 16:30-17:30 SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity Le Qi, Yu Zhang and Ting Liu 16:30-17:30 LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learn- ing to Rank Using Rich Features Naman Goyal 16:30-17:30 SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Ques- tions for Community Question Answering Delphine Charlet and Geraldine Damnati 16:30-17:30 FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Rele- vant Answers in Community Question Answering Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li and Zhaoyun Ding 16:30-17:30 KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering Simone Filice, Giovanni Da San Martino and Alessandro Moschitti 16:30-17:30 SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering Jan Milan Deriu and Mark Cieliebak 16:30-17:30 TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Re- trieval in Community QA Filip \u0160aina, Toni Kukurin, Lukrecija Pulji\u0107, Mladen Karan and Jan \u0160najder 16:30-17:30 GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora Nada Almarwani and Mona Diab 16:30-17:30 NLM_NIH at SemEval-2017 Task 3: from Question Entailment to Question Simi- larity for Community Question Answering Asma Ben Abacha and Dina Demner-Fushman 16:30-17:30 bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features Yuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi and Kohsuke Yanai xxvi",
	"links": null
	},
	"BIBREF114": {
	"ref_id": "b114",
	"title": "): semi-supervised end-to-end entity and relation extraction Waleed Ammar, Matthew Peters, Chandra Bhagavatula and Russell Power 15:00-15:15 LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives Julien Tourille",
	"authors": [],
	"year": 2017,
	"venue": "45 Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR. Gerasimos Lampouras and Andreas Vlachos",
	"volume": "5",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 11:00-12:30 Task Descriptions 11:00-11:15 SemEval-2017 Task 4: Sentiment Analysis in Twitter Sara Rosenthal, Noura Farra and Preslav Nakov 11:15-11:30 SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Keith Cortis, Andr\u00e9 Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh and Brian Davis 11:30-11:45 SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation Jonathan May and Jay Priyadarshi 11:45-12:00 SemEval 2017 Task 10: ScienceIE -Extracting Keyphrases and Relations from Sci- entific Publications Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman and Andrew McCallum 12:00-12:15 SemEval-2017 Task 11: End-User Development using Natural Language Juliano Sales, Siegfried Handschuh and Andr\u00e9 Freitas 12:15-12:30 SemEval-2017 Task 12: Clinical TempEval Steven Bethard, Guergana Savova, Martha Palmer and James Pustejovsky 12:30-14:00 Lunch 4 Aug 2017 (continued) 14:00-15:30 Best Of SemEval 14:00-14:15 BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs Mathieu Cliche 14:15-14:30 Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting senti- ment from financial news headlines Andrew Moore and Paul Rayson 14:30-14:45 Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR. Gerasimos Lampouras and Andreas Vlachos 14:45-15:00 The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction Waleed Ammar, Matthew Peters, Chandra Bhagavatula and Russell Power 15:00-15:15 LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Informa- tion Extraction from Clinical Narratives Julien Tourille, Olivier Ferret, Xavier Tannier and Aur\u00e9lie N\u00e9v\u00e9ol 15:30-16:00 Coffee 16:00-16:30 Discussion 16:30-17:30 Poster Session 16:30-17:30 OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El- Khoury, Ahmad Al Sallab, Hazem Hajj, Nizar Habash, Khaled Shaban and Wassim El-Hajj 16:30-17:30 NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis Edilson Anselmo Corr\u00eaa J\u00fanior, Vanessa Queiroz Marinho and Leandro Borges dos Santos 16:30-17:30 deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter Tzu-Hsuan Yang, Tzu-Hsuan Tseng and Chia-Ping Chen xxxi",
	"links": null
	},
	"BIBREF115": {
	"ref_id": "b115",
	"title": "Urena Lopez 16:30-17:30 HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors Abeed Sarker and Graciela Gonzalez 16:30-17:30 ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter Enkhzol Dovdon and Jos\u00e9 Saias 16:30-17:30 SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification Raphael Troncy, Enrico Palumbo, Efstratios Sygkounas and Giuseppe Rizzo 16:30-17:30 Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter Alon Rozental and Daniel Fleischer 16:30-17:30 TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier Naveen Kumar Laskari and Suresh Kumar Sanampudi 16:30-17:30 Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets Hala Mulki, Hatem Haddad",
	"authors": [
	{
	"first": "Arturo",
	"middle": [],
	"last": "Jim\u00e9nez-Zafra",
	"suffix": ""
	},
	{
	"first": "Maite",
	"middle": [],
	"last": "Montejo-R\u00e1ez",
	"suffix": ""
	},
	{
	"first": "L",
	"middle": [],
	"last": "Martin",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Alfonso",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "30 Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter Athanasia Kolovou, Filippos Kokkinos",
	"volume": "16",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Sim- ple Ensemble Method with Different Embeddings Yichun Yin, Yangqiu Song and Ming Zhang 16:30-17:30 CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sen- timent Classification and Quantification Raj Kumar Gupta and Yinping Yang 16:30-17:30 SINAI at SemEval-2017 Task 4: User based classification Salud Mar\u00eda Jim\u00e9nez-Zafra, Arturo Montejo-R\u00e1ez, Maite Martin and L. Alfonso Urena Lopez 16:30-17:30 HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors Abeed Sarker and Graciela Gonzalez 16:30-17:30 ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter Enkhzol Dovdon and Jos\u00e9 Saias 16:30-17:30 SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to En- hance Sentiment Classification Raphael Troncy, Enrico Palumbo, Efstratios Sygkounas and Giuseppe Rizzo 16:30-17:30 Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter Alon Rozental and Daniel Fleischer 16:30-17:30 TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradi- ent Boost Tree Classifier Naveen Kumar Laskari and Suresh Kumar Sanampudi 16:30-17:30 Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets Hala Mulki, Hatem Haddad, Mourad Gridach and Ismail Babaoglu 16:30-17:30 OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Ran- dom Fields Chukwuyem Onyibe and Nizar Habash 16:30-17:30 Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise clas- sification models for sentiment analysis in Twitter Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Nikolaos Malandrakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan and Alexandros Potamianos 16:30-17:30 NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture Nikolay Karpov xxxii",
	"links": null
	},
	"BIBREF116": {
	"ref_id": "b116",
	"title": "30 LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification Amal Htait, S\u00e9bastien Fournier and Patrice Bellot",
	"authors": [
	{
	"first": "Yan",
	"middle": [],
	"last": "Zhao",
	"suffix": ""
	},
	{
	"first": "Bing",
	"middle": [],
	"last": "Yang",
	"suffix": ""
	},
	{
	"first": "S",
	"middle": [],
	"last": "Xu ; Deborah",
	"suffix": ""
	},
	{
	"first": "Milton",
	"middle": [],
	"last": "Rajendram",
	"suffix": ""
	},
	{
	"first": "T T",
	"middle": [],
	"last": "Mirnalinee",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "",
	"volume": "16",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification Jingjing Zhao, Yan Yang and Bing Xu 16:30-17:30 SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features Mohammed Jabreel and Antonio Moreno 16:30-17:30 Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification Hussam Hamdan 16:30-17:30 DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sen- timent Analysis Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis and Avi Arampatzis 16:30-17:30 SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi- Kernel Gaussian Process Classifier Angel Deborah S, S Milton Rajendram and T T Mirnalinee 16:30-17:30 YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter Ming Wang, Biao Chu, Qingxun Liu and Xiaobing Zhou 16:30-17:30 LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification Amal Htait, S\u00e9bastien Fournier and Patrice Bellot 16:30-17:30 ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning Jos\u00e9-\u00c1ngel Gonz\u00e1lez, Ferran Pla and Llu\u00eds-F. Hurtado 16:30-17:30 XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twit- ter Yazhou Hao, YangYang Lan, Yufei Li and Chen Li 16:30-17:30 Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention Joosung Yoon, Kigon Lyu and Hyeoncheol Kim 16:30-17:30 EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic- based Sentiment Classification wang maoquan, Chen Shiyun, Xie yufei and Zhao lu 16:30-17:30 funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classifica- tion by Exploiting Word Embeddings, Text Features and Target Contexts Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang and Sameena Shah",
	"links": null
	},
	"BIBREF117": {
	"ref_id": "b117",
	"title": "Deriu and Mark Cieliebak 16:30-17:30 INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis Sabino Miranda-Jim\u00e9nez, Mario Graff, Eric Sadit Tellez and Daniela Moctezuma 16:30-17:30 BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches Deger Ayata",
	"authors": [],
	"year": 2017,
	"venue": "30 TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis Amit Ajit Deshmane and Jasper Friedrichs",
	"volume": "16",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis Christos Baziotis, Nikos Pelekis and Christos Doulkeridis 16:30-17:30 TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification Georgios Balikas 16:30-17:30 LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Clas- sification Mickael Rouvier 16:30-17:30 TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolu- tional Neural Network with Distant Supervision Simon M\u00fcller, Tobias Huonder, Jan Deriu and Mark Cieliebak 16:30-17:30 INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Pro- gramming for Twitter Sentiment Analysis Sabino Miranda-Jim\u00e9nez, Mario Graff, Eric Sadit Tellez and Daniela Moctezuma 16:30-17:30 BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches Deger Ayata, Murat Saraclar and Arzucan Ozgur 16:30-17:30 TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter David Lozi\u0107, Doria \u0160ari\u0107, Ivan Toki\u0107, Zoran Medi\u0107 and Jan \u0160najder 16:30-17:30 NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis Samhaa R. El-Beltagy, Mona El kalamawy and Abu Bakr Soliman 16:30-17:30 YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification Haowei Zhang, Jin Wang, Jixian Zhang and Xuejie Zhang 16:30-17:30 TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis Amit Ajit Deshmane and Jasper Friedrichs 16:30-17:30 UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twit- ter Jos\u00e9 Abreu, Iv\u00e1n Castro, Claudia Mart\u00ednez, Sebasti\u00e1n Oliva and Yoan Guti\u00e9rrez 16:30-17:30 ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learn- ing Methods for Twitter Message Polarity Classification Yunxiao Zhou, Man Lan and Yuanbin Wu xxxiv",
	"links": null
	},
	"BIBREF118": {
	"ref_id": "b118",
	"title": ":30 IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Zarmeen Nasim 16:30-17:30 HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods Tobias Cabanski",
	"authors": [
	{
	"first": "Lorenzo",
	"middle": [],
	"last": "Mansar",
	"suffix": ""
	},
	{
	"first": "Sira",
	"middle": [],
	"last": "Gatti",
	"suffix": ""
	},
	{
	"first": "Marco",
	"middle": [],
	"last": "Ferradans",
	"suffix": ""
	},
	{
	"first": "Jacopo",
	"middle": [],
	"last": "Guerini",
	"suffix": ""
	},
	{
	"first": "S",
	"middle": [],
	"last": "Staiano ; Deborah",
	"suffix": ""
	},
	{
	"first": "Milton",
	"middle": [],
	"last": "Rajendram",
	"suffix": ""
	},
	{
	"first": "T T",
	"middle": [],
	"last": "Mirnalinee",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "30 DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis and Avi Arampatzis",
	"volume": "5",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment to- wards Brands from Financial News Headlines Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini and Jacopo Staiano 16:30-17:30 SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model Angel Deborah S, S Milton Rajendram and T T Mirnalinee 16:30-17:30 IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Zarmeen Nasim 16:30-17:30 HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods Tobias Cabanski, Julia Romberg and Stefan Conrad 16:30-17:30 INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines Tiago Zini, Karin Becker and Marcelo Dias 16:30-17:30 HCS at SemEval-2017 Task 5: Polarity detection in business news using convolu- tional neural networks Lidia Pivovarova, Lloren\u00e7 Escoter, Arto Klami and Roman Yangarber 16:30-17:30 NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Chung-Chi Chen, Hen-Hsen Huang and Hsin-Hsi Chen 16:30-17:30 funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Finan- cial Microblogs Using Word Vectors Built from StockTwits and Twitter Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang and Xiaomo Liu 16:30-17:30 SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Fi- nancial Tweets Narges Tabari, Armin Seyeditabari and Wlodek Zadrozny 16:30-17:30 DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblog- ging and News Articles Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis and Avi Arampatzis 16:30-17:30 TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine- grained sentiment analysis of financial news Leon Rotim, Martin Tutek and Jan \u0160najder 16:30-17:30 UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Head- lines using Training Dataset Augmentation Vineet John and Olga Vechtomova xxxv",
	"links": null
	},
	"BIBREF119": {
	"ref_id": "b119",
	"title": ":30 ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain Mengxiao Jiang, Man Lan and Yuanbin Wu 16:30-17:30 IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann and Pushpak Bhattacharyya 16:30-17:30 IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis Deepanway Ghosal, Shobhit Bhatnagar, Md Shad Akhtar",
	"authors": [],
	"year": 2017,
	"venue": "30 RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation Normunds Gruzitis",
	"volume": "5",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks Sudipta Kar, Suraj Maharjan and Thamar Solorio 16:30-17:30 COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines Kim Schouten, Flavius Frasincar and Franciska de Jong 16:30-17:30 ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effec- tive Features for Fine-Grained Sentiment Analysis in Financial Domain Mengxiao Jiang, Man Lan and Yuanbin Wu 16:30-17:30 IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann and Pushpak Bhattacharyya 16:30-17:30 IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis Deepanway Ghosal, Shobhit Bhatnagar, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya 16:30-17:30 FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares and Eug\u00e9nio Oliveira 16:30-17:30 UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR Khoa Nguyen and Dang Nguyen 16:30-17:30 Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented At- tention Jan Buys and Phil Blunsom 16:30-17:30 FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers Simon Mille, Roberto Carlini, Alicia Burga and Leo Wanner 16:30-17:30 RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation Normunds Gruzitis, Didzis Gosko and Guntis Barzdins 16:30-17:30 The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Se- mantic Parsing Rik van Noord and Johan Bos 16:30-17:30 PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge Liang Wang and Sujian Li xxxvi",
	"links": null
	},
	"BIBREF120": {
	"ref_id": "b120",
	"title": "Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications Sijia Liu, Feichen Shen, Vipin Chaudhary and Hongfang Liu 16:30-17:30 Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator Roman Kern, Stefan Falk and Andi Rexha 16:30-17:30 NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents Biswanath Barik and Erwin Marsi 16:30-17:30 LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network V\u00edctor Su\u00e1rez-Paniagua",
	"authors": [
	{
	"first": "Erwin",
	"middle": [],
	"last": "Marsi",
	"suffix": ""
	},
	{
	"first": "Cristina",
	"middle": [],
	"last": "Kumar Sikdar",
	"suffix": ""
	},
	{
	"first": "Biswanath",
	"middle": [],
	"last": "Marco",
	"suffix": ""
	},
	{
	"first": "Rune",
	"middle": [],
	"last": "Barik",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Saetre",
	"suffix": ""
	}
	],
	"year": 2017,
	"venue": "30 MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks Ji Young Lee",
	"volume": "10",
	"issue": "",
	"pages": "30--47",
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	"num": null,
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	"raw_text": "Aug 2017 (continued) 16:30-17:30 NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik and Rune Saetre 16:30-17:30 EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION Steffen Eger, Erik-L\u00e2n Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha and Iryna Gurevych 16:30-17:30 LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publica- tions by combining the BANNER tool and the UMLS Semantic Network Isabel Segura-Bedmar, Crist\u00f3bal Col\u00f3n-Ruiz and Paloma Mart\u00ednez 16:30-17:30 The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields Lung-Hao Lee, Kuei-Ching Lee and Yuen-Hsien Tseng 16:30-17:30 MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications Sijia Liu, Feichen Shen, Vipin Chaudhary and Hongfang Liu 16:30-17:30 Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator Roman Kern, Stefan Falk and Andi Rexha 16:30-17:30 NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents Biswanath Barik and Erwin Marsi 16:30-17:30 LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network V\u00edctor Su\u00e1rez-Paniagua, Isabel Segura-Bedmar and Paloma Mart\u00ednez 16:30-17:30 WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling Animesh Prasad and Min-Yen Kan 16:30-17:30 MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Net- works Ji Young Lee, Franck Dernoncourt and Peter Szolovits 16:30-17:30 TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers Tomoki Tsujimura, Makoto Miwa and Yutaka Sasaki 16:30-17:30 SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation G\u00e1bor Berend xxxvii",
	"links": null
	},
	"BIBREF121": {
	"ref_id": "b121",
	"title": ":30 EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development Marek Kubis, Pawe\u0142 Sk\u00f3rzewski and Tomasz Zi\u0119tkiewicz 16:30-17:30 Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes Sarath P R, Manikandan R and Yoshiki Niwa 16:30-17:30 NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation",
	"authors": [],
	"year": 2017,
	"venue": "Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model",
	"volume": "16",
	"issue": "",
	"pages": "30--47",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Aug 2017 (continued) 16:30-17:30 LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model Simon David Hernandez, Davide Buscaldi and Thierry Charnois 16:30-17:30 EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End- User Development Marek Kubis, Pawe\u0142 Sk\u00f3rzewski and Tomasz Zi\u0119tkiewicz 16:30-17:30 Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes Sarath P R, Manikandan R and Yoshiki Niwa 16:30-17:30 NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang and Hsin-Hsi Chen 16:30-17:30 XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model Yu Long, Zhijing Li, Xuan Wang and Chen Li 16:30-17:30 ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal ex- pressions and events Andre Lamurias, Diana Sousa, Sofia Pereira, Luka Clarke and Francisco M Couto 16:30-17:30 GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Tempo- ral Information Extraction Sean MacAvaney, Arman Cohan and Nazli Goharian 16:30-17:30 KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records Artuur Leeuwenberg and Marie-Francine Moens xxxviii",
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	}
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	"text": "Semantic comparison for words and texts\u2022 Task 1: Semantic Textual Similarity\u2022 Task 2: Multi-lingual and Cross-lingual Semantic Word Similarity \u2022 Task 3: Community Question Answering Detecting sentiment, humor, and truth \u2022 Task 4: Sentiment Analysis in Twitter \u2022 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News \u2022 Task 6: #HashtagWars: Learning a Sense of Humor \u2022 Task 7: Detection and Interpretation of English Puns \u2022 Task 8: RumourEval: Determining rumour veracity and support for rumours Parsing semantic structures \u2022 Task 9: Abstract Meaning Representation Parsing and Generation \u2022 Task 10: Extracting Keyphrases and Relations from Scientific Publications \u2022 Task 11: End-User Development using Natural Language \u2022 Task 12: Clinical TempEval"
	},
	"FIGREF1": {
	"uris": null,
	"type_str": "figure",
	"num": null,
	"text": "The SemEval-2017 organizers, Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens"
	},
	"TABREF0": {
	"type_str": "table",
	"num": null,
	"html": null,
	"content": "<table><tr><td>3 August 2017 (continued) 3 August 2017 (continued)</td></tr><tr><td>16:30-17:30 UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Character-16:30-17:30 Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classifi-</td></tr><tr><td>istics and Corpus-based Metrics cation with Branch-LSTM</td></tr><tr><td>Olga Vechtomova Elena Kochkina, Maria Liakata and Isabelle Augenstein</td></tr><tr><td>16:30-17:30 PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic 16:30-17:30 Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules</td></tr><tr><td>Pun Recognition and Interpretation Marianela Garc\u00eda Lozano, Hanna Lilja, Edward Tj\u00f6rnhammar and Maja Karasalo</td></tr><tr><td>Guoshun Wu, Yixuan Sheng, Man Lan and Yuanbin Wu Elena Mikhalkova and Yuri Karyakin</td></tr><tr><td>16:30-17:30 DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using</td></tr><tr><td>16:30-17:30 UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels 16:30-17:30 JU CSE NLP @ SemEval 2017 Task 7: Employing Rules to Detect and Interpret Cascading Heuristics</td></tr><tr><td>for Similarity Features for CQA English Puns Ankit Srivastava, Georg Rehm and Julian Moreno Schneider</td></tr><tr><td>Surya Agustian and Hiroya Takamura Aniket Pramanick and Dipankar Das</td></tr><tr><td>16:30-17:30 ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and</td></tr><tr><td>16:30-17:30 Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase 16:30-17:30 N-Hance at SemEval-2017 Task 7: A Computational Approach using Word Associ-Supervised Ensemble Models</td></tr><tr><td>Detection ation for Puns Feixiang Wang, Man Lan and Yuanbin Wu</td></tr><tr><td>Byron Galbraith, Bhanu Pratap and Daniel Shank \u00d6zge Sevgili, Nima Ghotbi and Selma Tekir</td></tr><tr><td>16:30-17:30 IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation</td></tr><tr><td>16:30-17:30 QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor 16:30-17:30 ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation Vikram Singh, Sunny Narayan, Md Shad Akhtar, Asif Ekbal and Pushpak Bhat-</td></tr><tr><td>Analysis in Twitter Llu\u00eds-F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco and Jos\u00e9-\u00c1ngel tacharyya</td></tr><tr><td>Xiwu Han and Gregory Toner Gonz\u00e1lez</td></tr><tr><td>16:30-17:30 Duluth at SemEval-2017 Task 6: Language Models in Humor Detection 16:30-17:30 BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and 4 Aug 2017</td></tr><tr><td>Xinru Yan and Ted Pedersen Locating Homographic English Puns with Sense Embeddings</td></tr><tr><td>Dieke Oele and Kilian Evang</td></tr><tr><td>16:30-17:30 DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous 09:00-09:30 SemEval 2018 Tasks</td></tr><tr><td>Text Comparison 16:30-17:30 UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns</td></tr><tr><td>Christos Baziotis, Nikos Pelekis and Christos Doulkeridis Ankit Vadehra</td></tr><tr><td>09:30-10:30 State of SemEval Discussion</td></tr><tr><td>16:30-17:30 TakeLab at SemEval-2017 Task 6: #RankingHumorIn4Pages 16:30-17:30 ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to</td></tr><tr><td>Marin Kukova\u010dec, Juraj Malenica, Ivan Mr\u0161i\u0107, Antonio \u0160ajatovi\u0107, Domagoj Alagi\u0107 Detect and Locate English Puns</td></tr><tr><td>and Jan \u0160najder Yuhuan Xiu, Man Lan and Yuanbin Wu 10:30-11:00 Coffee</td></tr><tr><td>16:30-17:30 SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition 16:30-17:30 Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns</td></tr><tr><td>Andrew Cattle and Xiaojuan Ma in English Language</td></tr><tr><td>Vijayasaradhi Indurthi and Subba Reddy Oota</td></tr><tr><td>16:30-17:30 #WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Hu-</td></tr><tr><td>morous Tweets 16:30-17:30 UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic</td></tr><tr><td>Iuliana Alexandra Fle\u015fcan-Lovin-Arseni, Ramona Andreea Turcu, Cristina Sirbu, Independent Features</td></tr><tr><td>Larisa Alexa, Sandra Maria Amarandei, Nichita Herciu, Constantin Scutaru, Diana Hareesh Bahuleyan and Olga Vechtomova</td></tr><tr><td>Trandabat and Adrian Iftene</td></tr><tr><td>16:30-17:30 IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection</td></tr><tr><td>16:30-17:30 SVNIT @ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised and rumor verification</td></tr><tr><td>Approach Yi-Chin Chen, Zhao-Yang Liu and Hung-Yu Kao</td></tr><tr><td>Rutal Mahajan and Mukesh Zaveri</td></tr><tr><td>16:30-17:30 NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for</td></tr><tr><td>16:30-17:30 Duluth at SemEval-2017 Task 7 : Puns Upon a Midnight Dreary, Lexical Semantics Rumours on Twitter.</td></tr><tr><td>for the Weak and Weary Omar Enayet and Samhaa R. El-Beltagy</td></tr><tr><td>Ted Pedersen</td></tr><tr><td>xxvii xxviii</td></tr></table>",
	"text": "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation 16:30-17:30 QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums Marwan Torki, Maram Hasanain and Tamer Elsayed 16:30-17:30 ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task"
	}
	}
	}
	}