| { |
| "paper_id": "2021", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T16:21:02.450509Z" |
| }, |
| "title": "Amrita CEN NLP@DravidianLangTech-EACL2021: Deep Learning-based Offensive Language Identification in Malayalam, Tamil and Kannada", |
| "authors": [], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This paper describes the submission of the team Amrita CEN NLP to the shared task on Offensive Language Identification in Dravidian Languages at EACL 2021. We implemented three deep neural network architectures such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer and a hidden layer, a network containing a Bi-LSTM and another with a Bidirectional Recurrent Neural Network (Bi-RNN). In addition to that, we incorporated a cost-sensitive learning approach to deal with the problem of class imbalance in the training data. Among the three models, the hybrid network exhibited better training performance, and we submitted the predictions based on the same.", |
| "pdf_parse": { |
| "paper_id": "2021", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "This paper describes the submission of the team Amrita CEN NLP to the shared task on Offensive Language Identification in Dravidian Languages at EACL 2021. We implemented three deep neural network architectures such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer and a hidden layer, a network containing a Bi-LSTM and another with a Bidirectional Recurrent Neural Network (Bi-RNN). In addition to that, we incorporated a cost-sensitive learning approach to deal with the problem of class imbalance in the training data. Among the three models, the hybrid network exhibited better training performance, and we submitted the predictions based on the same.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "In recent years, people from all walks of life use social platforms like Twitter, Instagram, Facebook. So, it is demanding to monitor their behavior to avoid violence, hateful and offensive content Mahesan, 2019, 2020a,b) . Offensive content is any non-verbal or oral communication expressing disparity against a group or person based on their religion, age, sexual orientation, race, gender, nationality, and ethnicity (Chakravarthi and Muralidaran, 2021; Suryawanshi and Chakravarthi, 2021) .", |
| "cite_spans": [ |
| { |
| "start": 198, |
| "end": 221, |
| "text": "Mahesan, 2019, 2020a,b)", |
| "ref_id": null |
| }, |
| { |
| "start": 457, |
| "end": 492, |
| "text": "Suryawanshi and Chakravarthi, 2021)", |
| "ref_id": "BIBREF21" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "A substantial amount of work was done to identify offensive content in English, but much work is not done in Dravidian languages (Chakravarthi et al., 2018 (Chakravarthi et al., , 2019 Chakravarthi, 2020) . The Dravidian languages were first documented in Tamili script engraved on cave walls in Tamil Nadu's Madurai and Tirunelveli districts in the 6th century BCE. India being a multilingual country, a lot of people use regional languages along with English. The usage of two languages to communicate is called code-mixing. It is even more challenging to identify hateful content from code-mixed language owing to the non-standard grammar and spelling (Sreelakshmi et al., 2020) , (Sreelakshmi et al., 2019) , (Sasidhar et al., 2020) .", |
| "cite_spans": [ |
| { |
| "start": 129, |
| "end": 155, |
| "text": "(Chakravarthi et al., 2018", |
| "ref_id": "BIBREF4" |
| }, |
| { |
| "start": 156, |
| "end": 184, |
| "text": "(Chakravarthi et al., , 2019", |
| "ref_id": "BIBREF5" |
| }, |
| { |
| "start": 185, |
| "end": 204, |
| "text": "Chakravarthi, 2020)", |
| "ref_id": "BIBREF3" |
| }, |
| { |
| "start": 655, |
| "end": 681, |
| "text": "(Sreelakshmi et al., 2020)", |
| "ref_id": "BIBREF20" |
| }, |
| { |
| "start": 684, |
| "end": 710, |
| "text": "(Sreelakshmi et al., 2019)", |
| "ref_id": "BIBREF19" |
| }, |
| { |
| "start": 713, |
| "end": 736, |
| "text": "(Sasidhar et al., 2020)", |
| "ref_id": "BIBREF18" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "DravidianLangTech-EACL2021 is a task to identify offensive content from code-mixed Tamil-English (Tam-Eng), Malayalam-English (Mal-Eng), and Kannada-English (Kan-Eng). In this task, we came up with a Deep learning model to identify offensive content from Malayalam-English, Tamil-English, and Kannada-English datasets. We employed three different deep learning models for solving the classification problem. A hybrid model that includes a convolutional layer followed by a Bi-LSTM (Graves et al., 2013) , (Premjith et al., 2018) and a fully connected network attained the maximum scores while training. Therefore, the labels for the test data were predicted using the aforementioned model. The rest of the contents are explained in the following sections: Section 2 presents the literature review. Dataset details are provided in Section 3. Section 4 explains the system description, and section 5 relates to experimental details and results. Finally, the work is concluded in Section 6.", |
| "cite_spans": [ |
| { |
| "start": 481, |
| "end": 502, |
| "text": "(Graves et al., 2013)", |
| "ref_id": "BIBREF13" |
| }, |
| { |
| "start": 505, |
| "end": 528, |
| "text": "(Premjith et al., 2018)", |
| "ref_id": "BIBREF17" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "Different abusive and offense language identification problems and shared tasks have been explored in the literature ranging from aggression to cyberbullying, hate speech, toxic comments, and offensive language. Below we discuss each of them briefly.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Literature Review", |
| "sec_num": "2" |
| }, |
| { |
| "text": "In 2018, Adithya et.al (Bohra et al., 2018 ) evolved a dataset consisting of 4500 hate and nonhate code-mixed Hindi-English tweets. The dataset was congregated using Twitter API and annotated by two linguists. Machine learning models like Random Forest and SVM and handcrafted features like character N-Grams, punctuation count, emoticon count, negation words, word N-Grams were used for classification.", |
| "cite_spans": [ |
| { |
| "start": 23, |
| "end": 42, |
| "text": "(Bohra et al., 2018", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Literature Review", |
| "sec_num": "2" |
| }, |
| { |
| "text": "SemEval (Zampieri et al., 2019) conducted three tasks in 2019, of which one task is on offensive and non-offensive comments detection from English tweets. The dataset (OLID) used for the task has 13240 tweets for training and 860 tweets for testing. Several models like Convolutional Neural Networks (CNN), Bidirectional Encoder Representations from Transformers (BERT), Long Short Term Memory (LSTM), LSTM with attention, Embeddings from Language Models (ELMo) were used by various teams. Even basic machine learning models like SVM was a part of the assorted models used. SemEval 2020 conducted a task on offensive language identification in multilingual languages (Of-fensEval) such as English, Arabic, Danish, Greek, and Turkish. The same task was also conducted for Indo-European languages in FIRE 2019 (Mandl et al., 2019) .", |
| "cite_spans": [ |
| { |
| "start": 8, |
| "end": 31, |
| "text": "(Zampieri et al., 2019)", |
| "ref_id": "BIBREF25" |
| }, |
| { |
| "start": 808, |
| "end": 828, |
| "text": "(Mandl et al., 2019)", |
| "ref_id": "BIBREF16" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Literature Review", |
| "sec_num": "2" |
| }, |
| { |
| "text": "In 2020, FIRE conducted a shared task called Hate Speech and Offensive Content Identification from code-mixed posts in Dravidian languages (Malayalam-English and Tamil-English) (Chakravarthi et al., 2020d; Mandl et al., 2020; Chakravarthi et al., 2020b) . Different teams came up with diversified approaches of which include, the work by Gaurav Arora (Arora, 2020). He came up with an approach based on a pretraining ULM-FiT on code-mixed data, which are generated synthetically. The code-mixed data was modeled as a Markov process using Markov chains.", |
| "cite_spans": [ |
| { |
| "start": 177, |
| "end": 205, |
| "text": "(Chakravarthi et al., 2020d;", |
| "ref_id": "BIBREF12" |
| }, |
| { |
| "start": 206, |
| "end": 225, |
| "text": "Mandl et al., 2020;", |
| "ref_id": "BIBREF15" |
| }, |
| { |
| "start": 226, |
| "end": 253, |
| "text": "Chakravarthi et al., 2020b)", |
| "ref_id": "BIBREF7" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Literature Review", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The dataset (Chakravarthi et al., 2021), (Chakravarthi et al., 2020a), (Chakravarthi et al., 2020c) , (Hande et al., 2020) consists of sentences from three code-mixed languages namely Tamil-English, Malayalam-English and Kannada-English. The Kannada-English and Tamil-English dataset have sentences labeled to six classes and, the Malayalam-English dataset has five labels. The labels for each language are given in Table 1 and the dataset statistics is given in Table 2. ", |
| "cite_spans": [ |
| { |
| "start": 71, |
| "end": 99, |
| "text": "(Chakravarthi et al., 2020c)", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 102, |
| "end": 122, |
| "text": "(Hande et al., 2020)", |
| "ref_id": "BIBREF14" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 416, |
| "end": 423, |
| "text": "Table 1", |
| "ref_id": "TABREF1" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Dataset Description", |
| "sec_num": "3" |
| }, |
| { |
| "text": "This section describes the details of the model submitted to the shared task. We have experimented with various deep neural networks for identifying the underlying patterns in the text required for classification.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "System Description", |
| "sec_num": "4" |
| }, |
| { |
| "text": "The dataset provided for the shared tasks contains words in both native languages (Malayalam, Tamil, and Kannada) and English. The dataset comprises social media texts and hence includes user names, hashtags, and URLs. Since these entities do not contribute much to the classification task, we employed a preprocessing step to remove such entities from the text. In addition to that, the preprocessing step involved steps to remove the punctuation and to lower-case the English characters.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Preprocessing", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "We experimented with different deep learning models for classifying the social media text into different categories. The model which obtained the highest accuracy when tested with the validation data is a hybrid of a 1-D convolution layer, a 1-D max-pooling layer, a Bidirectional-LSTM, and a fully connected network along with another fully connected layer dedicated for classification, and is illustrated in Figure 1 . The same model is used for all the classification tasks. The other models considered are a network containing one Bi-LSTM layer and another with a Bi-RNN layer.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 410, |
| "end": 418, |
| "text": "Figure 1", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "The cleaned text is fed into the model after another sequence of preprocessing steps which involve the following,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 Tokenization: An \"<OOV>\" token is used to mark the Out-of-Vocabulary (OOV) words in the test data.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 Translation of words into indexes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 Padding the sequences with zeros to nake the sequence length equal: Here, maximum sequence length is set to the length of the lengthiest sentence in the dataset. Here, the zeros are padded at the end of the sequences. This padded sequences are fed into the model.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "The dataset used for this task is highly imbalanced. To reduce the bias towards the majority class, we applied a cost-sensitive learning approach. This approach computes weights for each class so that the majority class gets minimum weight, and the minority class gets maximum weight. Equation 1 is used for computing the class weights.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "cw = N N c", |
| "eq_num": "(1)" |
| } |
| ], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Where cw is the class weight, N is the total number of data points in the corpus, and N c is the number of sentences in the class c. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Models", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Hyperparameter tuning is a crucial step in building a deep learning model. The performance of a deep learning model heavily relies on the optimal selection of the hyperparameters. In this model, we chose the hyperparameters from a set of values based on the metrics considered for evaluating the model. The metrics used in this model are accuracy, precision, and recall, and AUC. The hyperparameters were chosen based on the performance of the model on validation data. A grid search method was used to find the optimal hyperparameters from a set of values. The set of optimal hyperparameters for this model are shown in Table 3 . We used the same model for all the tasks and hence didn't change the hyperparameters for individual tasks.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 621, |
| "end": 628, |
| "text": "Table 3", |
| "ref_id": "TABREF4" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Hyperparameter Tuning", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "We experimented with three deep learning models for three subtasks in the shared task. The first model, Model-1 is a hybrid of CNN, Bi-LSTM, and a fully connected layer apart from the output layer, the second model, Model-2, has a Bi-LSTM layer, and the third model, Model-3, is made up of a Bi-RNN layer. We used validation data to evaluate the models to identify the best performing model. The performance was measured using metrics such as accuracy, precision, recall, and AUC. Among the three models, the network containing CNN+Bi-LSTM+Dense layers achieved the best scores. Even though all the models exhibited comparable accuracy, the decisive factor was the recall score. The hybrid model performed substantially better than the other two models in terms of recall and precision. Besides that, it is also evident that the hybrid model could use the class weights effectively. This trend is visible in all three tasks. Tables 4, 5, and 6 shows the training performance of the Malayalam-English dataset, Tamil-English dataset and Kannada-English dataset, respectively. We submitted the predictions obtained by Model-1 based on the training performance.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Results and Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "The performance of the submitted model over the testing data is given in Table 7 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 73, |
| "end": 80, |
| "text": "Table 7", |
| "ref_id": "TABREF9" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Results and Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "This paper presents the submission of Amrita CEN NLP to the shared task at EACL 2021 on Offensive Language Identification from three Dravidian Languages, namely Tamil-English (Tam-Eng), Malayalam-English (Mal-Eng), and Kannada-English (Kan-Eng). Three Deep Learning architectures, such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer, and a fully connected network, a network containing a Bi-LSTM, and another with a BidirectionalRecurrent Neural Network (Bi-RNN) were implemented. The class imbalance problem was solved using the costsensitive learning approach. The hybrid of CNN, Bi-LSTM, and a fully-connected layer model gave the highest result of 90% accuracy for Mal-Eng, 64% accuracy for Tam-Eng, and 65% accuracy for kan-Eng.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusion", |
| "sec_num": "6" |
| } |
| ], |
| "back_matter": [], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Gaurav Arora. 2020. Gauravarora@ HASOC-Dravidian-CodeMix-FIRE2020: Pre-training ULM-FiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": { |
| "arXiv": [ |
| "arXiv:2010.02094" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Gaurav Arora. 2020. Gauravarora@ HASOC- Dravidian-CodeMix-FIRE2020: Pre-training ULM- FiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection. arXiv preprint arXiv:2010.02094.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "A dataset of hindi-english code-mixed social media text for hate speech detection", |
| "authors": [], |
| "year": null, |
| "venue": "Proceedings of the second workshop on computational modeling of people's opinions, personality, and emotions in social media", |
| "volume": "", |
| "issue": "", |
| "pages": "36--41", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "A dataset of hindi-english code-mixed social media text for hate speech detection. In Proceedings of the second workshop on computational modeling of people's opinions, personality, and emotions in social media, pages 36-41.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Leveraging orthographic information to improve machine translation of under-resourced languages", |
| "authors": [ |
| { |
| "first": "Chakravarthi", |
| "middle": [], |
| "last": "Bharathi Raja", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi. 2020. Leveraging ortho- graphic information to improve machine translation of under-resourced languages. Ph.D. thesis, NUI Galway.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Improving wordnets for underresourced languages using machine translation", |
| "authors": [ |
| { |
| "first": "Mihael", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "P" |
| ], |
| "last": "Arcan", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Mccrae", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 9th Global Wordnet Conference", |
| "volume": "", |
| "issue": "", |
| "pages": "77--86", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Mihael Arcan, and John P. McCrae. 2018. Improving wordnets for under- resourced languages using machine translation. In Proceedings of the 9th Global Wordnet Conference, pages 77-86, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "WordNet gloss translation for underresourced languages using multilingual neural machine translation", |
| "authors": [ |
| { |
| "first": "Mihael", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "P" |
| ], |
| "last": "Arcan", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Mccrae", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation", |
| "volume": "", |
| "issue": "", |
| "pages": "1--7", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Mihael Arcan, and John P. McCrae. 2019. WordNet gloss translation for under- resourced languages using multilingual neural ma- chine translation. In Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation, pages 1-7, Dublin, Ireland. European Association for Ma- chine Translation.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "A sentiment analysis dataset for codemixed Malayalam-English", |
| "authors": [ |
| { |
| "first": "Navya", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "Shardul", |
| "middle": [], |
| "last": "Jose", |
| "suffix": "" |
| }, |
| { |
| "first": "Elizabeth", |
| "middle": [], |
| "last": "Suryawanshi", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "Philip" |
| ], |
| "last": "Sherly", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Mc-Crae", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", |
| "volume": "", |
| "issue": "", |
| "pages": "177--184", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, Elizabeth Sherly, and John Philip Mc- Crae. 2020a. A sentiment analysis dataset for code- mixed Malayalam-English. In Proceedings of the 1st Joint Workshop on Spoken Language Technolo- gies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 177-184, Marseille, France. European Language Resources association.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "Overview of the track on HASOC-Offensive Language Identification-DravidianCodeMix", |
| "authors": [ |
| { |
| "first": "Anand", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [], |
| "last": "Kumar", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Philip Mccrae", |
| "suffix": "" |
| }, |
| { |
| "first": "B", |
| "middle": [], |
| "last": "Premjith", |
| "suffix": "" |
| }, |
| { |
| "first": "K", |
| "middle": [ |
| "P" |
| ], |
| "last": "Soman", |
| "suffix": "" |
| }, |
| { |
| "first": "Thomas", |
| "middle": [], |
| "last": "Mandl", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Working Notes of the Forum for Information Retrieval Evaluation", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, M Anand Kumar, John Philip McCrae, Premjith B, Soman KP, and Thomas Mandl. 2020b. Overview of the track on HASOC-Offensive Language Identification- DravidianCodeMix. In Working Notes of the Forum for Information Retrieval Evaluation (FIRE 2020).", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "CEUR Workshop Proceedings", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "CEUR Workshop Proceedings. In: CEUR-WS. org, Hyderabad, India.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "Findings of the shared task on Hope Speech Detection for Equality, Diversity, and Inclusion", |
| "authors": [ |
| { |
| "first": "Vigneshwaran", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Muralidaran", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi and Vigneshwaran Mural- idaran. 2021. Findings of the shared task on Hope Speech Detection for Equality, Diversity, and Inclu- sion. In Proceedings of the First Workshop on Lan- guage Technology for Equality, Diversity and Inclu- sion. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Corpus creation for sentiment analysis in code-mixed Tamil-English text", |
| "authors": [ |
| { |
| "first": "Vigneshwaran", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "Ruba", |
| "middle": [], |
| "last": "Muralidaran", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "Philip" |
| ], |
| "last": "Priyadharshini", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Mc-Crae", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", |
| "volume": "", |
| "issue": "", |
| "pages": "202--210", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Vigneshwaran Murali- daran, Ruba Priyadharshini, and John Philip Mc- Crae. 2020c. Corpus creation for sentiment anal- ysis in code-mixed Tamil-English text. In Pro- ceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced lan- guages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 202-210, Marseille, France. European Language Re- sources association.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "Findings of the shared task on Offensive Language Identification in Tamil, Malayalam, and Kannada", |
| "authors": [ |
| { |
| "first": "Ruba", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "Navya", |
| "middle": [], |
| "last": "Priyadharshini", |
| "suffix": "" |
| }, |
| { |
| "first": "Anand", |
| "middle": [], |
| "last": "Jose", |
| "suffix": "" |
| }, |
| { |
| "first": "M", |
| "middle": [], |
| "last": "Kumar", |
| "suffix": "" |
| }, |
| { |
| "first": "Thomas", |
| "middle": [], |
| "last": "Mandl", |
| "suffix": "" |
| }, |
| { |
| "first": "Prasanna", |
| "middle": [], |
| "last": "Kumar Kumaresan", |
| "suffix": "" |
| }, |
| { |
| "first": "Rahul", |
| "middle": [], |
| "last": "Ponnusamy", |
| "suffix": "" |
| }, |
| { |
| "first": "V", |
| "middle": [], |
| "last": "Hariharan", |
| "suffix": "" |
| }, |
| { |
| "first": "Elizabeth", |
| "middle": [], |
| "last": "Sherly", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "Philip" |
| ], |
| "last": "Mc-Crae", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Navya Jose, Anand Kumar M, Thomas Mandl, Prasanna Kumar Kumaresan, Rahul Ponnusamy, Hariharan V, Elizabeth Sherly, and John Philip Mc- Crae. 2021. Findings of the shared task on Offen- sive Language Identification in Tamil, Malayalam, and Kannada. In Proceedings of the First Workshop on Speech and Language Technologies for Dravid- ian Languages. Association for Computational Lin- guistics.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Overview of the Track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text", |
| "authors": [ |
| { |
| "first": "Ruba", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| }, |
| { |
| "first": "Vigneshwaran", |
| "middle": [], |
| "last": "Priyadharshini", |
| "suffix": "" |
| }, |
| { |
| "first": "Shardul", |
| "middle": [], |
| "last": "Muralidaran", |
| "suffix": "" |
| }, |
| { |
| "first": "Navya", |
| "middle": [], |
| "last": "Suryawanshi", |
| "suffix": "" |
| }, |
| { |
| "first": "Elizabeth", |
| "middle": [], |
| "last": "Jose", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [ |
| "P" |
| ], |
| "last": "Sherly", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Mccrae", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "In Forum for Information Retrieval Evaluation", |
| "volume": "2020", |
| "issue": "", |
| "pages": "21--24", |
| "other_ids": { |
| "DOI": [ |
| "10.1145/3441501.3441515" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Vigneshwaran Muralidaran, Shardul Suryawanshi, Navya Jose, Elizabeth Sherly, and John P. McCrae. 2020d. Overview of the Track on Sentiment Analy- sis for Dravidian Languages in Code-Mixed Text. In Forum for Information Retrieval Evaluation, FIRE 2020, page 21-24, New York, NY, USA. Associa- tion for Computing Machinery.", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Speech recognition with deep recurrent neural networks", |
| "authors": [ |
| { |
| "first": "Alex", |
| "middle": [], |
| "last": "Graves", |
| "suffix": "" |
| }, |
| { |
| "first": "Mohamed", |
| "middle": [], |
| "last": "Abdel-Rahman", |
| "suffix": "" |
| }, |
| { |
| "first": "Geoffrey", |
| "middle": [], |
| "last": "Hinton", |
| "suffix": "" |
| } |
| ], |
| "year": 2013, |
| "venue": "2013 IEEE international conference on acoustics, speech and signal processing", |
| "volume": "", |
| "issue": "", |
| "pages": "6645--6649", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recur- rent neural networks. In 2013 IEEE international conference on acoustics, speech and signal process- ing, pages 6645-6649. Ieee.", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection", |
| "authors": [ |
| { |
| "first": "Adeep", |
| "middle": [], |
| "last": "Hande", |
| "suffix": "" |
| }, |
| { |
| "first": "Ruba", |
| "middle": [], |
| "last": "Priyadharshini", |
| "suffix": "" |
| }, |
| { |
| "first": "Bharathi Raja", |
| "middle": [], |
| "last": "Chakravarthi", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media", |
| "volume": "", |
| "issue": "", |
| "pages": "54--63", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Adeep Hande, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2020. KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection. In Proceedings of the Third Workshop on Computational Modeling of Peo- ple's Opinions, Personality, and Emotion's in Social Media, pages 54-63, Barcelona, Spain (Online). Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF15": { |
| "ref_id": "b15", |
| "title": "Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil", |
| "authors": [ |
| { |
| "first": "Thomas", |
| "middle": [], |
| "last": "Mandl", |
| "suffix": "" |
| }, |
| { |
| "first": "Sandip", |
| "middle": [], |
| "last": "Modha", |
| "suffix": "" |
| }, |
| { |
| "first": "Anand", |
| "middle": [], |
| "last": "Kumar", |
| "suffix": "" |
| }, |
| { |
| "first": "M", |
| "middle": [], |
| "last": "", |
| "suffix": "" |
| }, |
| { |
| "first": "Bharathi Raja Chakravarthi ;", |
| "middle": [], |
| "last": "Malayalam", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Hindi", |
| "suffix": "" |
| }, |
| { |
| "first": "German", |
| "middle": [], |
| "last": "English", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Forum for Information Retrieval Evaluation", |
| "volume": "2020", |
| "issue": "", |
| "pages": "29--32", |
| "other_ids": { |
| "DOI": [ |
| "10.1145/3441501.3441517" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Thomas Mandl, Sandip Modha, Anand Kumar M, and Bharathi Raja Chakravarthi. 2020. Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malay- alam, Hindi, English and German. In Forum for Information Retrieval Evaluation, FIRE 2020, page 29-32, New York, NY, USA. Association for Com- puting Machinery.", |
| "links": null |
| }, |
| "BIBREF16": { |
| "ref_id": "b16", |
| "title": "Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages", |
| "authors": [ |
| { |
| "first": "Thomas", |
| "middle": [], |
| "last": "Mandl", |
| "suffix": "" |
| }, |
| { |
| "first": "Sandip", |
| "middle": [], |
| "last": "Modha", |
| "suffix": "" |
| }, |
| { |
| "first": "Prasenjit", |
| "middle": [], |
| "last": "Majumder", |
| "suffix": "" |
| }, |
| { |
| "first": "Daksh", |
| "middle": [], |
| "last": "Patel", |
| "suffix": "" |
| }, |
| { |
| "first": "Mohana", |
| "middle": [], |
| "last": "Dave", |
| "suffix": "" |
| }, |
| { |
| "first": "Chintak", |
| "middle": [], |
| "last": "Mandlia", |
| "suffix": "" |
| }, |
| { |
| "first": "Aditya", |
| "middle": [], |
| "last": "Patel", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the 11th Forum for Information Retrieval Evaluation", |
| "volume": "", |
| "issue": "", |
| "pages": "14--17", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Thomas Mandl, Sandip Modha, Prasenjit Majumder, Daksh Patel, Mohana Dave, Chintak Mandlia, and Aditya Patel. 2019. Overview of the hasoc track at fire 2019: Hate speech and offensive content identi- fication in indo-european languages. In Proceedings of the 11th Forum for Information Retrieval Evalua- tion, pages 14-17.", |
| "links": null |
| }, |
| "BIBREF17": { |
| "ref_id": "b17", |
| "title": "A deep learning based Part-of-Speech (POS) tagger for Sanskrit language by embedding character level features", |
| "authors": [ |
| { |
| "first": "B", |
| "middle": [], |
| "last": "Premjith", |
| "suffix": "" |
| }, |
| { |
| "first": "Prabaharan", |
| "middle": [], |
| "last": "Soman", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Poornachandran", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "FIRE", |
| "volume": "", |
| "issue": "", |
| "pages": "56--60", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "B Premjith, KP Soman, and Prabaharan Poornachan- dran. 2018. A deep learning based Part-of-Speech (POS) tagger for Sanskrit language by embedding character level features. In FIRE, pages 56-60.", |
| "links": null |
| }, |
| "BIBREF18": { |
| "ref_id": "b18", |
| "title": "Emotion Detection in Hinglish (Hindi+ English) Code-Mixed Social Media Text. Procedia Computer Science", |
| "authors": [ |
| { |
| "first": "B", |
| "middle": [], |
| "last": "T Tulasi Sasidhar", |
| "suffix": "" |
| }, |
| { |
| "first": "K", |
| "middle": [ |
| "P" |
| ], |
| "last": "Premjith", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Soman", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "", |
| "volume": "171", |
| "issue": "", |
| "pages": "1346--1352", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "T Tulasi Sasidhar, B Premjith, and KP Soman. 2020. Emotion Detection in Hinglish (Hindi+ English) Code-Mixed Social Media Text. Procedia Com- puter Science, 171:1346-1352.", |
| "links": null |
| }, |
| "BIBREF19": { |
| "ref_id": "b19", |
| "title": "Amrita CEN at HASOC 2019: Hate Speech Detection in Roman and Devanagiri Scripted Text", |
| "authors": [ |
| { |
| "first": "K", |
| "middle": [], |
| "last": "Sreelakshmi", |
| "suffix": "" |
| }, |
| { |
| "first": "K", |
| "middle": [ |
| "P" |
| ], |
| "last": "Premjith", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Soman", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "FIRE (Working Notes)", |
| "volume": "", |
| "issue": "", |
| "pages": "366--369", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "K Sreelakshmi, B Premjith, and KP Soman. 2019. Am- rita CEN at HASOC 2019: Hate Speech Detection in Roman and Devanagiri Scripted Text. In FIRE (Working Notes), pages 366-369.", |
| "links": null |
| }, |
| "BIBREF20": { |
| "ref_id": "b20", |
| "title": "Detection of Hate Speech Text in Hindi-English Codemixed Data", |
| "authors": [ |
| { |
| "first": "K", |
| "middle": [], |
| "last": "Sreelakshmi", |
| "suffix": "" |
| }, |
| { |
| "first": "K", |
| "middle": [ |
| "P" |
| ], |
| "last": "Premjith", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Soman", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Procedia Computer Science", |
| "volume": "171", |
| "issue": "", |
| "pages": "737--744", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "K Sreelakshmi, B Premjith, and KP Soman. 2020. De- tection of Hate Speech Text in Hindi-English Code- mixed Data. Procedia Computer Science, 171:737- 744.", |
| "links": null |
| }, |
| "BIBREF21": { |
| "ref_id": "b21", |
| "title": "Findings of the shared task on Troll Meme Classification in Tamil", |
| "authors": [ |
| { |
| "first": "Shardul", |
| "middle": [], |
| "last": "Suryawanshi", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Bharathi Raja Chakravarthi", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Shardul Suryawanshi and Bharathi Raja Chakravarthi. 2021. Findings of the shared task on Troll Meme Classification in Tamil. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Compu- tational Linguistics.", |
| "links": null |
| }, |
| "BIBREF22": { |
| "ref_id": "b22", |
| "title": "Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation", |
| "authors": [ |
| { |
| "first": "Sajeetha", |
| "middle": [], |
| "last": "Thavareesan", |
| "suffix": "" |
| }, |
| { |
| "first": "Sinnathamby", |
| "middle": [], |
| "last": "Mahesan", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "2019 14th Conference on Industrial and Information Systems (ICIIS)", |
| "volume": "", |
| "issue": "", |
| "pages": "320--325", |
| "other_ids": { |
| "DOI": [ |
| "10.1109/ICIIS47346.2019.9063341" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2019. Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Rep- resentation. In 2019 14th Conference on Industrial and Information Systems (ICIIS), pages 320-325.", |
| "links": null |
| }, |
| "BIBREF23": { |
| "ref_id": "b23", |
| "title": "Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts", |
| "authors": [ |
| { |
| "first": "Sajeetha", |
| "middle": [], |
| "last": "Thavareesan", |
| "suffix": "" |
| }, |
| { |
| "first": "Sinnathamby", |
| "middle": [], |
| "last": "Mahesan", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "2020 Moratuwa Engineering Research Conference (MERCon)", |
| "volume": "", |
| "issue": "", |
| "pages": "272--276", |
| "other_ids": { |
| "DOI": [ |
| "10.1109/MERCon50084.2020.9185369" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2020a. Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts. In 2020 Moratuwa Engineering Re- search Conference (MERCon), pages 272-276.", |
| "links": null |
| }, |
| "BIBREF24": { |
| "ref_id": "b24", |
| "title": "Word embedding-based Part of Speech tagging in Tamil texts", |
| "authors": [ |
| { |
| "first": "Sajeetha", |
| "middle": [], |
| "last": "Thavareesan", |
| "suffix": "" |
| }, |
| { |
| "first": "Sinnathamby", |
| "middle": [], |
| "last": "Mahesan", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)", |
| "volume": "", |
| "issue": "", |
| "pages": "478--482", |
| "other_ids": { |
| "DOI": [ |
| "10.1109/ICIIS51140.2020.9342640" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2020b. Word embedding-based Part of Speech tag- ging in Tamil texts. In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), pages 478-482.", |
| "links": null |
| }, |
| "BIBREF25": { |
| "ref_id": "b25", |
| "title": "Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)", |
| "authors": [ |
| { |
| "first": "Marcos", |
| "middle": [], |
| "last": "Zampieri", |
| "suffix": "" |
| }, |
| { |
| "first": "Shervin", |
| "middle": [], |
| "last": "Malmasi", |
| "suffix": "" |
| }, |
| { |
| "first": "Preslav", |
| "middle": [], |
| "last": "Nakov", |
| "suffix": "" |
| }, |
| { |
| "first": "Sara", |
| "middle": [], |
| "last": "Rosenthal", |
| "suffix": "" |
| }, |
| { |
| "first": "Noura", |
| "middle": [], |
| "last": "Farra", |
| "suffix": "" |
| }, |
| { |
| "first": "Ritesh", |
| "middle": [], |
| "last": "Kumar", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": { |
| "arXiv": [ |
| "arXiv:1903.08983" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019. Semeval-2019 task 6: Identifying and cate- gorizing offensive language in social media (offen- seval). arXiv preprint arXiv:1903.08983.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "uris": null, |
| "type_str": "figure", |
| "num": null, |
| "text": "An illustration of the deep learning model submitted to the shared task" |
| }, |
| "TABREF1": { |
| "type_str": "table", |
| "content": "<table><tr><td colspan=\"4\">Language Train set Valid set Test set</td></tr><tr><td>Mal-Eng</td><td>16010</td><td>1999</td><td>2001</td></tr><tr><td>Tam-Eng</td><td>35139</td><td>4388</td><td>4392</td></tr><tr><td>Kan-Eng</td><td>6217</td><td>777</td><td>778</td></tr></table>", |
| "text": "Details of the class labels available in the dataset.", |
| "num": null, |
| "html": null |
| }, |
| "TABREF2": { |
| "type_str": "table", |
| "content": "<table/>", |
| "text": "", |
| "num": null, |
| "html": null |
| }, |
| "TABREF4": { |
| "type_str": "table", |
| "content": "<table/>", |
| "text": "Set of hyperparameters used in building the model.", |
| "num": null, |
| "html": null |
| }, |
| "TABREF6": { |
| "type_str": "table", |
| "content": "<table><tr><td>Model</td><td colspan=\"3\">Accuracy Precision Recall AUC</td></tr><tr><td colspan=\"2\">Model-1 0.8932</td><td>0.6936</td><td>0.6438 0.8872</td></tr><tr><td colspan=\"2\">Model-2 0.8680</td><td>0.6371</td><td>0.4829 0.8456</td></tr><tr><td colspan=\"2\">Model-3 0.8364</td><td>0.9100</td><td>0.0207 0.8801</td></tr></table>", |
| "text": "Training performance of various models experimented for the Malayalam-English data.", |
| "num": null, |
| "html": null |
| }, |
| "TABREF7": { |
| "type_str": "table", |
| "content": "<table><tr><td>Model</td><td>Accuracy PrecisionRecall AUC</td></tr><tr><td colspan=\"2\">Model-1 0.8795 0.6951 0.4929 0.8396</td></tr><tr><td colspan=\"2\">Model-2 0.8567 0.5767 0.5277 0.8469</td></tr><tr><td colspan=\"2\">Model-3 0.8368 0.5113 0.4672 0.7891</td></tr></table>", |
| "text": "Training performance of various models experimented for the Tamil-English data.", |
| "num": null, |
| "html": null |
| }, |
| "TABREF8": { |
| "type_str": "table", |
| "content": "<table><tr><td>Dataset</td><td colspan=\"3\">Precision Recall F1-score</td></tr><tr><td colspan=\"2\">Mal-Eng 0.90</td><td>0.82</td><td>0.85</td></tr><tr><td colspan=\"2\">Tam-Eng 0.64</td><td>0.62</td><td>0.62</td></tr><tr><td colspan=\"2\">Kan-Eng 0.65</td><td>0.54</td><td>0.58</td></tr></table>", |
| "text": "Training performance of various models experimented for the Kannada-English data.", |
| "num": null, |
| "html": null |
| }, |
| "TABREF9": { |
| "type_str": "table", |
| "content": "<table/>", |
| "text": "Performance of the model over the test data.", |
| "num": null, |
| "html": null |
| } |
| } |
| } |
| } |
