Anupam Jamatia


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NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to Tackle Internet Humor
Steve Durairaj Swamy | Shubham Laddha | Basil Abdussalam | Debayan Datta | Anupam Jamatia
Proceedings of the Fourteenth Workshop on Semantic Evaluation

The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the ‘NIT-Agartala-NLP-Team’. A dataset of 8879 memes was made available by the task organizers to train and test our models. Our systems include a Logistic Regression baseline, a BiLSTM +Attention-based learner and a transfer learning approach with BERT. For the three sub-tasks A, B and C, we attained ranks 24/33, 11/29 and 15/26, respectively. We highlight our difficulties in harnessing image information as well as some techniques and handcrafted features we employ to overcome these issues. We also discuss various modelling issues and theorize possible solutions and reasons as to why these problems persist.


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Studying Generalisability across Abusive Language Detection Datasets
Steve Durairaj Swamy | Anupam Jamatia | Björn Gambäck
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Work on Abusive Language Detection has tackled a wide range of subtasks and domains. As a result of this, there exists a great deal of redundancy and non-generalisability between datasets. Through experiments on cross-dataset training and testing, the paper reveals that the preconceived notion of including more non-abusive samples in a dataset (to emulate reality) may have a detrimental effect on the generalisability of a model trained on that data. Hence a hierarchical annotation model is utilised here to reveal redundancies in existing datasets and to help reduce redundancy in future efforts.

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Validation of Facts Against Textual Sources
Vamsi Krishna Pendyala | Simran Sinha | Satya Prakash | Shriya Reddy | Anupam Jamatia
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In today’s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49%

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NIT_Agartala_NLP_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media Corpora
Steve Durairaj Swamy | Anupam Jamatia | Björn Gambäck | Amitava Das
Proceedings of the 13th International Workshop on Semantic Evaluation

The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on ‘Identifying and Categorizing Offensive Language in Social Media’ by the ‘NIT_Agartala_NLP_Team’. A Twitter annotated dataset of 13,240 English tweets was provided by the task organizers to train the individual models, with the best results obtained using an ensemble model composed of six different classifiers. The ensemble model produced macro-averaged F1-scores of 0.7434, 0.7078 and 0.4853 on Subtasks A, B, and C, respectively. The paper highlights the overall low predictive nature of various linguistic features and surface level count features, as well as the limitations of a traditional machine learning approach when compared to a Deep Learning counterpart.


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A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content
Tushar Maheshwari | Aishwarya N. Reganti | Samiksha Gupta | Anupam Jamatia | Upendra Kumar | Björn Gambäck | Amitava Das
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

To find out how users’ social media behaviour and language are related to their ethical practices, the paper investigates applying Schwartz’ psycholinguistic model of societal sentiment to social media text. The analysis is based on corpora collected from user essays as well as social media (Facebook and Twitter). Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.


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Part-of-Speech Tagging for Code-Mixed English-Hindi Twitter and Facebook Chat Messages
Anupam Jamatia | Björn Gambäck | Amitava Das
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Sentence Boundary Detection for Social Media Text
Dwijen Rudrapal | Anupam Jamatia | Kunal Chakma | Amitava Das | Björn Gambäck
Proceedings of the 12th International Conference on Natural Language Processing