Amitava Das


2020

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SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
Chhavi Sharma | Deepesh Bhageria | William Scott | Srinivas PYKL | Amitava Das | Tanmoy Chakraborty | Viswanath Pulabaigari | Björn Gambäck
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes- with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes,overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.

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SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
Parth Patwa | Gustavo Aguilar | Sudipta Kar | Suraj Pandey | Srinivas PYKL | Björn Gambäck | Tanmoy Chakraborty | Thamar Solorio | Amitava Das
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.

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Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling
Arindam Chatterjere | Vineeth Guptha | Parul Chopra | Amitava Das
Proceedings of the 12th Language Resources and Evaluation Conference

Code-Mixing (CM) or language mixing is a social norm in multilingual societies. CM is quite prevalent in social media conversations in multilingual regions like - India, Europe, Canada and Mexico. In this paper, we explore the problem of Language Modeling (LM) for code-mixed Hinglish text. In recent times, there have been several success stories with neural language modeling like Generative Pre-trained Transformer (GPT) (Radford et al., 2019), Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) etc.. Hence, neural language models have become the new holy grail of modern NLP, although LM for CM is an unexplored area altogether. To better understand the problem of LM for CM, we initially experimented with several statistical language modeling techniques and consequently experimented with contemporary neural language models. Analysis shows switching-points are the main challenge for the LMCM performance drop, therefore in this paper we introduce the idea of minority positive sampling to selectively induce more sample to achieve better performance. On the contrary, all neural language models demand a huge corpus to train on for better performance. Finally, we are reporting a perplexity of 139 for Hinglish (Hindi-English language pair) LMCM using statistical bi-directional techniques.

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Proceedings of the The 4th Workshop on Computational Approaches to Code Switching
Thamar Solorio | Monojit Choudhury | Kalika Bali | Sunayana Sitaram | Amitava Das | Mona Diab
Proceedings of the The 4th Workshop on Computational Approaches to Code Switching

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Aggression and Misogyny Detection using BERT: A Multi-Task Approach
Niloofar Safi Samghabadi | Parth Patwa | Srinivas PYKL | Prerana Mukherjee | Amitava Das | Thamar Solorio
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on “Aggression Identification” (sub-task A) and “Misogynistic Aggression Identification” (sub-task B). The data for this shared task is provided in three different languages - English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes - Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes - Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, “na14”, scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media

2019

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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.

2017

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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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Measuring the Limit of Semantic Divergence for English Tweets.
Dwijen Rudrapal | Amitava Das
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

In human language, an expression could be conveyed in many ways by different people. Even that the same person may express same sentence quite differently when addressing different audiences, using different modalities, or using different syntactic variations or may use different set of vocabulary. The possibility of such endless surface form of text while the meaning of the text remains almost same, poses many challenges for Natural Language Processing (NLP) systems like question-answering system, machine translation system and text summarization. This research paper is an endeavor to understand the characteristic of such endless semantic divergence. In this research work we develop a corpus of 1525 semantic divergent sentences for 200 English tweets.

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“A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty” – Understanding the psycho-sociological influences to it
Updendra Kumar | Vishal Kumar Rana | Srinivas PYKL | Amitava Das
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

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“Who Mentions Whom?”- Understanding the Psycho-Sociological Aspects of Twitter Mention Network
R Sudhesh Solomon | Abhay Narayan | Srinivas P Y K L | Amitava Das
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2016

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Comparing the Level of Code-Switching in Corpora
Björn Gambäck | Amitava Das
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Social media texts are often fairly informal and conversational, and when produced by bilinguals tend to be written in several different languages simultaneously, in the same way as conversational speech. The recent availability of large social media corpora has thus also made large-scale code-switched resources available for research. The paper addresses the issues of evaluation and comparison these new corpora entail, by defining an objective measure of corpus level complexity of code-switched texts. It is also shown how this formal measure can be used in practice, by applying it to several code-switched corpora.

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Cosmopolitan Mumbai, Orthodox Delhi, Techcity Bangalore:Understanding City Specific Societal Sentiment
Aishwarya N Reganti | Tushar Maheshwari | Upendra Kumar | Amitava Das
Proceedings of the 13th International Conference on Natural Language Processing

2015

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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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Measuring Semantic Similarity for Bengali Tweets Using WordNet
Dwijen Rudrapal | Amitava Das | Baby Bhattacharya
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

2014

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Code Mixing: A Challenge for Language Identification in the Language of Social Media
Utsab Barman | Amitava Das | Joachim Wagner | Jennifer Foster
Proceedings of the First Workshop on Computational Approaches to Code Switching

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A Framework for Health Behavior Change using Companionable Robots
Bandita Sarma | Amitava Das | Rodney Nielsen
Proceedings of the 8th International Natural Language Generation Conference (INLG)

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Identifying Languages at the Word Level in Code-Mixed Indian Social Media Text
Amitava Das | Björn Gambäck
Proceedings of the 11th International Conference on Natural Language Processing

2012

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Sentimantics: Conceptual Spaces for Lexical Sentiment Polarity Representation with Contextuality
Amitava Das | Björn Gambäck
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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A Light Weight Stemmer in Kokborok
Braja Gopal Patra | Khumbar Debbarma | Swapan Debbarma | Dipankar Das | Amitava Das | Sivaji Bandyopadhyay
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)

2011

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PsychoSentiWordNet
Amitava Das
Proceedings of the ACL 2011 Student Session

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Dr Sentiment Knows Everything!
Amitava Das | Sivaji Bandyopadhyay
Proceedings of the ACL-HLT 2011 System Demonstrations

2010

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Towards the Global SentiWordNet
Amitava Das | Sivaji Bandyopadhyay
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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English to Indian Languages Machine Transliteration System at NEWS 2010
Amitava Das | Tanik Saikh | Tapabrata Mondal | Asif Ekbal | Sivaji Bandyopadhyay
Proceedings of the 2010 Named Entities Workshop

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SentiWordNet for Indian Languages
Amitava Das | Sivaji Bandyopadhyay
Proceedings of the Eighth Workshop on Asian Language Resouces

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SemanticNet-Perception of Human Pragmatics
Amitava Das | Sivaji Bandyopadhyay
Proceedings of the 2nd Workshop on Cognitive Aspects of the Lexicon

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Clause Identification and Classification in Bengali
Aniruddha Ghosh | Amitava Das | Sivaji Bandyopadhyay
Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing

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JU_CSE_GREC10: Named Entity Generation at GREC 2010
Amitava Das | Tanik Saikh | Tapabrata Mondal | Sivaji Bandyopadhyay
Proceedings of the 6th International Natural Language Generation Conference

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Topic-Based Bengali Opinion Summarization
Amitava Das | Sivaji Bandyopadhyay
Coling 2010: Posters

2009

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English to Hindi Machine Transliteration System at NEWS 2009
Amitava Das | Asif Ekbal | Tapabrata Mondal | Sivaji Bandyopadhyay
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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Language Independent Named Entity Recognition in Indian Languages
Asif Ekbal | Rejwanul Haque | Amitava Das | Venkateswarlu Poka | Sivaji Bandyopadhyay
Proceedings of the IJCNLP-08 Workshop on Named Entity Recognition for South and South East Asian Languages