id stringlengths 4 4 | title stringlengths 22 113 | abstract stringlengths 282 2.29k | keyphrases sequence | prmu sequence | lvl-1 stringlengths 16.7k 86.2k | lvl-2 stringlengths 9.61k 76k | lvl-3 stringlengths 1.52k 23.8k | lvl-4 stringlengths 1.28k 19.2k |
|---|---|---|---|---|---|---|---|---|
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] | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution ∗ Adam I. Juda Division of Engineering and Applied Sciences Harvard University, Harvard Business School ajuda@hbs.edu David C. Parkes Division of Engineering and Applied Sciences Harvard University parkes@eecs.harvard.edu ABSTRACT Bidders on e... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... |
I-54 | Approximate and Online Multi-Issue Negotiation | "This paper analyzes bilateral multi-issue negotiation between self-interested autonomous agents. Th(...TRUNCATED) | ["approxim","negoti","time constraint","equilibrium","strategi","rel error","interact kei form","mul(...TRUNCATED) | [
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I-68 | "On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Const(...TRUNCATED) | "Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination proble(...TRUNCATED) | ["decentr markov decis process","decentr markov decis process","tempor constraint","agent-coordin pr(...TRUNCATED) | [
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I-55 | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory | "It is well established by conflict theorists and others that successful negotiation should incorpor(...TRUNCATED) | ["autom negoti","negoti","creat valu","claim valu","mediat","ineffici compromis","dilemma","concess"(...TRUNCATED) | [
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] | "Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory (...TRUNCATED) | "Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory\(...TRUNCATED) | "Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory\(...TRUNCATED) | "Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory\(...TRUNCATED) |
J-38 | Multi-Attribute Coalitional Games | "We study coalitional games where the value of cooperation among the agents are solely determined by(...TRUNCATED) | ["multi-attribut coalit game","coalit game","cooper","agent","divers econom interact","comput comple(...TRUNCATED) | [
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] | "Multi-Attribute Coalitional Games∗ Samuel Ieong † Computer Science Department Stanford Universi(...TRUNCATED) | "Multi-Attribute Coalitional Games * t\nABSTRACT\nWe study coalitional games where the value of coop(...TRUNCATED) | "Multi-Attribute Coalitional Games * t\nABSTRACT\nWe study coalitional games where the value of coop(...TRUNCATED) | "Multi-Attribute Coalitional Games * t\nABSTRACT\nWe study coalitional games where the value of coop(...TRUNCATED) |
I-57 | "Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System(...TRUNCATED) | "In this paper we develop a novel probabilistic model of computational trust that explicitly deals w(...TRUNCATED) | ["multi-dimension trust","reput system","correl","dirichlet distribut","rumour propag","anonym","ove(...TRUNCATED) | [
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I-43 | Dynamics Based Control with an Application to Area-Sweeping Problems | "In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an (...TRUNCATED) | ["dynam base control","dynam base control","control","area-sweep problem","stochast environ","partia(...TRUNCATED) | [
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I-42 | A Complete Distributed Constraint Optimization Method For Non-Traditional Pseudotree Arrangements | "Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems i(...TRUNCATED) | ["distribut constraint optim","pseudotre arrang","agent","maximum sequenti path cost","cross-edg pse(...TRUNCATED) | [
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I-56 | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework | "This paper presents a novel, unified distributed constraint satisfaction framework based on automat(...TRUNCATED) | ["constraint","algorithm","bdi","negoti","distribut constraint satisfact problem","dcsp","share envi(...TRUNCATED) | [
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I-52 | A Unified and General Framework for Argumentation-based Negotiation | "This paper proposes a unified and general framework for argumentation-based negotiation, in which t(...TRUNCATED) | ["framework","argument","argument","negoti","outcom","theori","agent","argument-base negoti","conces(...TRUNCATED) | [
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] | "A Unified and General Framework for Argumentation-based Negotiation Leila Amgoud IRIT - CNRS 118, r(...TRUNCATED) | "A Unified and General Framework for Argumentation-based Negotiation\nABSTRACT\nThis paper proposes (...TRUNCATED) | "A Unified and General Framework for Argumentation-based Negotiation\nABSTRACT\nThis paper proposes (...TRUNCATED) | "A Unified and General Framework for Argumentation-based Negotiation\nABSTRACT\nThis paper proposes (...TRUNCATED) |
YAML Metadata Warning:The task_categories "text-mining" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_ids "keyphrase-generation" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata Warning:The task_ids "keyphrase-extraction" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation
About
SemEval-2010 is a dataset for benchmarking keyphrase extraction and generation models. The dataset is composed of 244 full-text scientific papers collected from the ACM Digital Library. Keyphrases were annotated by readers and combined with those provided by the authors. Details about the SemEval-2010 dataset can be found in the original paper (kim et al., 2010).
This version of the dataset was produced by (Boudin et al., 2016) and provides four increasingly sophisticated levels of document preprocessing:
lvl-1: default text files provided by the SemEval-2010 organizers.lvl-2: for each file, we manually retrieved the original PDF file from the ACM Digital Library. We then extract the enriched textual content of the PDF files using an Optical Character Recognition (OCR) system and perform document logical structure detection using ParsCit v110505. We use the detected logical structure to remove author-assigned keyphrases and select only relevant elements : title, headers, abstract, introduction, related work, body text and conclusion. We finally apply a systematic dehyphenation at line breaks.slvl-3: we further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.lvl-4: we abridge the input text from level 3 preprocessed documents using an unsupervised summarization technique. We keep the title and abstract and select the most content bearing sentences from the remaining contents.
Titles and abstracts, collected from the SciCorefCorpus, are also provided. Details about how they were extracted and cleaned up can be found in (Chaimongkol et al., 2014).
Reference keyphrases are provided in stemmed form (because they were provided like this for the test split in the competition).
They are also categorized under the PRMU (Present-Reordered-Mixed-Unseen) scheme as proposed in (Boudin and Gallina, 2021).
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in prmu.py.
The Present reference keyphrases are also ordered by their order of apparition in the concatenation of title and text (lvl-1).
Content and statistics
The dataset is divided into the following two splits:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
|---|---|---|---|---|---|---|---|
| Train | 144 | 184.6 | 15.44 | 42.16 | 7.36 | 26.85 | 23.63 |
| Test | 100 | 203.1 | 14.66 | 40.11 | 8.34 | 27.12 | 24.43 |
Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers.
The following data fields are available :
- id: unique identifier of the document.
- title: title of the document.
- abstract: abstract of the document.
- lvl-1: content of the document with no text processing.
- lvl-2: content of the document retrieved from original PDF files and cleaned up.
- lvl-3: content of the document further abridged to relevant sections.
- lvl-4: content of the document further abridged using an unsupervised summarization technique.
- keyphrases: list of reference keyphrases.
- prmu: list of Present-Reordered-Mixed-Unseen categories for reference keyphrases.
References
- (Kim et al., 2010) Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010. SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 21–26, Uppsala, Sweden. Association for Computational Linguistics.
- (Chaimongkol et al., 2014) Panot Chaimongkol, Akiko Aizawa, and Yuka Tateisi. 2014. Corpus for Coreference Resolution on Scientific Papers. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3187–3190, Reykjavik, Iceland. European Language Resources Association (ELRA).
- (Boudin et al., 2016) Florian Boudin, Hugo Mougard, and Damien Cram. 2016. How Document Pre-processing affects Keyphrase Extraction Performance. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021. Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
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