Animesh Mukherjee


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hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification
Mithun Das | Somnath Banerjee | Animesh Mukherjee
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, “Troll Meme Classification in DravidianLangTech2022” at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Further fusing these two modalities help us achieve stable outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.

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CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
Souvic Chakraborty | Parag Dutta | Sumegh Roychowdhury | Animesh Mukherjee
Findings of the Association for Computational Linguistics: NAACL 2022

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, their proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content.Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using User Anchored self-supervision and contextual regularization.Our proposed approach secures ~1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular English language social networking datasets.


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A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification
Arijit Nag | Bidisha Samanta | Animesh Mukherjee | Niloy Ganguly | Soumen Chakrabarti
Proceedings of the 25th Conference on Computational Natural Language Learning

Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English. Despite recent interest in deep generative models for Indian languages, relation classification is still not well-served by public data sets. In response, we present IndoRE, a dataset with 39K entity- and relation-tagged gold sentences in three Indian languages, plus English. We start with a multilingual BERT (mBERT) based system that captures entity span positions and type information and provides competitive monolingual relation classification. Using this system, we explore and compare transfer mechanisms between languages. In particular, we study the accuracy-efficiency tradeoff between expensive gold instances vs. translated and aligned ‘silver’ instances.

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Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection
Debjoy Saha | Naman Paharia | Debajit Chakraborty | Punyajoy Saha | Animesh Mukherjee
Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages

Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task “Offensive Language Identification in Dravidian Languages” at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.


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NwQM: A neural quality assessment framework for Wikipedia
Bhanu Prakash Reddy Guda | Sasi Bhushan Seelaboyina | Soumya Sarkar | Animesh Mukherjee
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.

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Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection
Srijan Bansal | Vishal Garimella | Ayush Suhane | Jasabanta Patro | Animesh Mukherjee
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode various switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.


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KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.
Jasabanta Patro | Nitin Choudhary | Kalpit Chittora | Animesh Mukherjee
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our approach to solve Semeval task 3: EmoContext; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify the emotion associated with the utterance as one of the following emotion classes: Happy, Sad, Angry or Others. To solve this problem, we experiment with different deep learning models ranging from simple bidirectional LSTM (Long and short term memory) model to comparatively complex attention model. We also experiment with word embedding conceptnet along with word embedding generated from bi-directional LSTM taking input characters. We fine-tune different parameters and hyper-parameters associated with each of our models and report the value of evaluating measure i.e. micro precision along with class wise precision, recall and F1-score of each system. We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0.7261. We also report class wise precision, recall, and f1-score of best performing model along with other models that we have experimented with.

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A deep-learning framework to detect sarcasm targets
Jasabanta Patro | Srijan Bansal | Animesh Mukherjee
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.

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On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
Abhik Jana | Dima Puzyrev | Alexander Panchenko | Pawan Goyal | Chris Biemann | Animesh Mukherjee
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.

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StRE: Self Attentive Edit Quality Prediction in Wikipedia
Soumya Sarkar | Bhanu Prakash Reddy | Sandipan Sikdar | Animesh Mukherjee
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing toward developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing ∼ 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin – at least 17% and at most 103%. Our pre-trained model achieves such result after retraining on a set as small as 20% of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia.


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Deep Learning for Social Media Health Text Classification
Santosh Tokala | Vaibhav Gambhir | Animesh Mukherjee
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

This paper describes the systems developed for 1st and 2nd tasks of the 3rd Social Media Mining for Health Applications Shared Task at EMNLP 2018. The first task focuses on automatic detection of posts mentioning a drug name or dietary supplement, a binary classification. The second task is about distinguishing the tweets that present personal medication intake, possible medication intake and non-intake. We performed extensive experiments with various classifiers like Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDT) and deep learning architectures such as Long Short-Term Memory Networks (LSTM), jointed Convolutional Neural Networks (CNN) and LSTM architecture, and attention based LSTM architecture both at word and character level. We have also explored using various pre-trained embeddings like Global Vectors for Word Representation (GloVe), Word2Vec and task-specific embeddings learned using CNN-LSTM and LSTMs.

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CL Scholar: The ACL Anthology Knowledge Graph Miner
Mayank Singh | Pradeep Dogga | Sohan Patro | Dhiraj Barnwal | Ritam Dutt | Rajarshi Haldar | Pawan Goyal | Animesh Mukherjee
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We present CL Scholar, the ACL Anthology knowledge graph miner to facilitate high-quality search and exploration of current research progress in the computational linguistics community. In contrast to previous works, periodically crawling, indexing and processing of new incoming articles is completely automated in the current system. CL Scholar utilizes both textual and network information for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 scholarly natural language queries along with standard keyword-based search on constructed knowledge graph. It answers binary, statistical and list based natural language queries. The current system is deployed at We also provide REST API support along with bulk download facility. Our code and data are available at

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WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
Abhik Jana | Pranjal Kanojiya | Pawan Goyal | Animesh Mukherjee
Proceedings of the 27th International Conference on Computational Linguistics

The exponential increase in the usage of Wikipedia as a key source of scientific knowledge among the researchers is making it absolutely necessary to metamorphose this knowledge repository into an integral and self-contained source of information for direct utilization. Unfortunately, the references which support the content of each Wikipedia entity page, are far from complete. Why are the reference section ill-formed for most Wikipedia pages? Is this section edited as frequently as the other sections of a page? Can there be appropriate surrogates that can automatically enhance the reference section? In this paper, we propose a novel two step approach – WikiRef – that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks. In the first step, we build a classifier to ascertain whether a wikilink is a potential source of reference or not. In the following step, we recommend references to the target page from the reference section of the wikilinks that are classified as potential sources of references in the first step. We perform an extensive evaluation of our approach on datasets from two different domains – Computer Science and Physics. For Computer Science we achieve a notably good performance with a precision@1 of 0.44 for reference recommendation as opposed to 0.38 obtained from the most competitive baseline. For the Physics dataset, we obtain a similar performance boost of 10% with respect to the most competitive baseline.


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All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media
Jasabanta Patro | Bidisha Samanta | Saurabh Singh | Abhipsa Basu | Prithwish Mukherjee | Monojit Choudhury | Animesh Mukherjee
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

n this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman’s correlation values, our methods perform more than two times better (∼ 0.62) in predicting the borrowing likeliness compared to the best performing baseline (∼ 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88% of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.

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Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
Binny Mathew | Suman Kalyan Maity | Pratip Sarkar | Animesh Mukherjee | Pawan Goyal
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing

Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominant and novel-sense discovery algorithms to identify these corpus-specific senses. We make use of text data available in the form of millions of digitized books and newspaper archives as two different sources of corpora and propose automated methods to identify corpus-specific word senses at various time points. We conduct an extensive and thorough human judgement experiment to rigorously evaluate and compare the performance of these approaches. Post adaptation, the output of the three algorithms are in the same format and the accuracy results are also comparable, with roughly 45-60% of the reported corpus-specific senses being judged as genuine.


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OCR++: A Robust Framework For Information Extraction from Scholarly Articles
Mayank Singh | Barnopriyo Barua | Priyank Palod | Manvi Garg | Sidhartha Satapathy | Samuel Bushi | Kumar Ayush | Krishna Sai Rohith | Tulasi Gamidi | Pawan Goyal | Animesh Mukherjee
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set of scientific articles written in English to understand generic writing patterns and formulate rules to develop this hybrid framework. Extensive evaluations show that the proposed framework outperforms the existing state-of-the-art tools by a large margin in structural information extraction along with improved performance in metadata and bibliography extraction tasks, both in terms of accuracy (around 50% improvement) and processing time (around 52% improvement). A user experience study conducted with the help of 30 researchers reveals that the researchers found this system to be very helpful. As an additional objective, we discuss two novel use cases including automatically extracting links to public datasets from the proceedings, which would further accelerate the advancement in digital libraries. The result of the framework can be exported as a whole into structured TEI-encoded documents. Our framework is accessible online at


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That’s sick dude!: Automatic identification of word sense change across different timescales
Sunny Mitra | Ritwik Mitra | Martin Riedl | Chris Biemann | Animesh Mukherjee | Pawan Goyal
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Global topology of word co-occurrence networks: Beyond the two-regime power-law
Monojit Choudhury | Diptesh Chatterjee | Animesh Mukherjee
Coling 2010: Posters


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Discovering Global Patterns in Linguistic Networks through Spectral Analysis: A Case Study of the Consonant Inventories
Animesh Mukherjee | Monojit Choudhury | Ravi Kannan
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Language Diversity across the Consonant Inventories: A Study in the Framework of Complex Networks
Monojit Choudhury | Animesh Mukherjee | Anupam Basu | Niloy Ganguly | Ashish Garg | Vaibhav Jalan
Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition

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Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)
Monojit Choudhury | Samer Hassan | Animesh Mukherjee | Smaranda Muresan
Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)

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Syntax is from Mars while Semantics from Venus! Insights from Spectral Analysis of Distributional Similarity Networks
Chris Biemann | Monojit Choudhury | Animesh Mukherjee
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers


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Social Network Inspired Models of NLP and Language Evolution
Monojit Choudhury | Animesh Mukherjee | Niloy Ganguly
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Unsupervised Parts-of-Speech Induction for Bengali
Joydeep Nath | Monojit Choudhury | Animesh Mukherjee | Christian Biemann | Niloy Ganguly
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present a study of the word interaction networks of Bengali in the framework of complex networks. The topological properties of these networks reveal interesting insights into the morpho-syntax of the language, whereas clustering helps in the induction of the natural word classes leading to a principled way of designing POS tagsets. We compare different network construction techniques and clustering algorithms based on the cohesiveness of the word clusters. Cohesiveness is measured against two gold-standard tagsets by means of the novel metric of tag-entropy. The approach presented here is a generic one that can be easily extended to any language.

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Modeling the Structure and Dynamics of the Consonant Inventories: A Complex Network Approach
Animesh Mukherjee | Monojit Choudhury | Anupam Basu | Niloy Ganguly
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)


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How Difficult is it to Develop a Perfect Spell-checker? A Cross-Linguistic Analysis through Complex Network Approach
Monojit Choudhury | Markose Thomas | Animesh Mukherjee | Anupam Basu | Niloy Ganguly
Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing

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Emergence of Community Structures in Vowel Inventories: An Analysis Based on Complex Networks
Animesh Mukherjee | Monojit Choudhury | Anupam Basu | Niloy Ganguly
Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology

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Redundancy Ratio: An Invariant Property of the Consonant Inventories of the World’s Languages
Animesh Mukherjee | Monojit Choudhury | Anupam Basu | Niloy Ganguly
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics


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Analysis and Synthesis of the Distribution of Consonants over Languages: A Complex Network Approach
Monojit Choudhury | Animesh Mukherjee | Anupam Basu | Niloy Ganguly
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions