no code implementations • ACL 2014 • Sunny Mitra, Ritwik Mitra, Martin Riedl, Chris Biemann, Animesh Mukherjee, Pawan Goyal
In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books.
no code implementations • 31 Jan 2016 • Suman Kalyan Maity, Chaitanya Sarda, Anshit Chaudhary, Abhijeet Patil, Shraman Kumar, Akash Mondal, Animesh Mukherjee
Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version.
no code implementations • 23 Aug 2016 • Soham Dan, Sanyam Agarwal, Mayank Singh, Pawan Goyal, Animesh Mukherjee
Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas.
no code implementations • 11 Mar 2017 • Suman Kalyan Maity, Aman Kharb, Animesh Mukherjee
Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74. 18%.
no code implementations • 15 Mar 2017 • Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
We first propose context based clustering method to sample a set of candidate words from the social media data. Next, we propose three novel and similar metrics based on the usage of these words by the users in different tweets; these metrics were used to score and rank the candidate words indicating their borrowed likeliness.
no code implementations • 25 Jul 2017 • Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Abhipsa Basu, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
no code implementations • EMNLP 2017 • Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Abhipsa Basu, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
no code implementations • 10 Sep 2017 • Mayank Singh, Soham Dan, Sanyam Agarwal, Pawan Goyal, Animesh Mukherjee
We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article.
no code implementations • WS 2017 • Binny Mathew, Suman Kalyan Maity, Pratip Sarkar, Animesh Mukherjee, Pawan Goyal
Word senses are not static and may have temporal, spatial or corpus-specific scopes.
no code implementations • 13 Feb 2018 • Mayank Singh, Rajdeep Sarkar, Atharva Vyas, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti
We propose several approaches to rank papers from these noisy 'match' outcomes.
no code implementations • COLING 2018 • Abhik Jana, Pranjal Kanojiya, Pawan Goyal, Animesh Mukherjee
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.
no code implementations • WS 2018 • Santosh Tokala, Vaibhav Gambhir, Animesh Mukherjee
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.
no code implementations • 17 Nov 2018 • Binny Mathew, Ritam Dutt, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherjee
In particular, we observe that the choice to post the question as anonymous is dependent on the user's perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity.
no code implementations • 4 Dec 2018 • Binny Mathew, Ritam Dutt, Pawan Goyal, Animesh Mukherjee
The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront.
Social and Information Networks
no code implementations • 6 Dec 2018 • Binny Mathew, Navish Kumar, Ravina, Pawan Goyal, Animesh Mukherjee
We also build a supervised model for classifying the hateful and counterspeech accounts on Twitter and obtain an F-score of 0. 77.
Social and Information Networks
no code implementations • 14 Dec 2018 • Abhik Jana, Animesh Mukherjee, Pawan Goyal
The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure.
2 code implementations • 17 Dec 2018 • Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee
With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content.
no code implementations • 7 Feb 2019 • Abhisek Dash, Animesh Mukherjee, Saptarshi Ghosh
In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users.
1 code implementation • 10 Mar 2019 • Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, Animesh Mukherjee
In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow.
no code implementations • SEMEVAL 2019 • Jasabanta Patro, Nitin Choudhary, Kalpit Chittora, Animesh Mukherjee
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.
no code implementations • 7 Jun 2019 • Abhik Jana, Dmitry Puzyrev, Alexander Panchenko, Pawan Goyal, Chris Biemann, Animesh Mukherjee
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar\'e embeddings in addition to the distributional information to detect compositionality for noun phrases.
1 code implementation • ACL 2019 • Soumya Sarkar, Bhanu Prakash Reddy, Sandipan Sikdar, Animesh Mukherjee
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.
no code implementations • ACL 2019 • Abhik Jana, Dima Puzyrev, Alex Panchenko, er, Pawan Goyal, Chris Biemann, Animesh Mukherjee
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar{\'e} embeddings in addition to the distributional information to detect compositionality for noun phrases.
no code implementations • 10 Sep 2019 • Binny Mathew, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherjee
Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year.
1 code implementation • 27 Sep 2019 • Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee
In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019.
no code implementations • IJCNLP 2019 • Jasabanta Patro, Srijan Bansal, Animesh Mukherjee
In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
3 code implementations • 14 Apr 2020 • Sai Saketh Aluru, Binny Mathew, Punyajoy Saha, Animesh Mukherjee
Hate speech detection is a challenging problem with most of the datasets available in only one language: English.
no code implementations • ACL 2020 • Srijan Bansal, Vishal Garimella, Ayush Suhane, Jasabanta Patro, Animesh Mukherjee
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications.
1 code implementation • 5 Jun 2020 • Souvic Chakraborty, Pawan Goyal, Animesh Mukherjee
We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair.
no code implementations • 5 Jun 2020 • Sayantan Adak, Atharva Vyas, Animesh Mukherjee, Heer Ambavi, Pritam Kadasi, Mayank Singh, Shivam Patel
We introduce an AI-enabled portal that presents an excellent visualization of Mahatma Gandhi's life events by constructing temporal and spatial social networks from the Gandhian literature.
no code implementations • 20 Jul 2020 • Jagriti Jalal, Mayank Singh, Arindam Pal, Lipika Dey, Animesh Mukherjee
Understanding the topical evolution in industrial innovation is a challenging problem.
6 code implementations • 18 Dec 2020 • Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, Animesh Mukherjee
We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities.
Ranked #3 on Hate Speech Detection on HateXplain
no code implementations • 21 Jan 2021 • Rima Hazra, Hardik Aggarwal, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti
This "social network of code" is rarely studied by social network researchers.
no code implementations • 30 Jan 2021 • Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.
2 code implementations • 7 Feb 2021 • Punyajoy Saha, Binny Mathew, Kiran Garimella, Animesh Mukherjee
We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community.
1 code implementation • EACL (DravidianLangTech) 2021 • Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee
Social media often acts as breeding grounds for different forms of offensive content.
1 code implementation • 21 Jul 2021 • Srijan Bansal, Vishal Garimella, Ayush Suhane, Animesh Mukherjee
In this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting.
1 code implementation • 1 Aug 2021 • Mithun Das, Punyajoy Saha, Ritam Dutt, Pawan Goyal, Animesh Mukherjee, Binny Mathew
Hate speech is regarded as one of the crucial issues plaguing the online social media.
no code implementations • 21 Sep 2021 • Paramita Das, Bhanu Prakash Reddy Guda, Debajit Chakraborty, Soumya Sarkar, Animesh Mukherjee
Success of planetary-scale online collaborative platforms such as Wikipedia is hinged on active and continued participation of its voluntary contributors.
no code implementations • 18 Oct 2021 • Arijit Nag, Bidisha Samanta, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation.
1 code implementation • 2 Nov 2021 • Paramita Das, Bhanu Prakash Reddy Guda, Sasi Bhusan Seelaboyina, Soumya Sarkar, Animesh Mukherjee
To the best of our knowledge, this is the first work that rigorously explores English Wikipedia article quality life cycle from the perspective of quality indicators and provides a novel unsupervised page level approach to detect quality switch, which can help in automatic content monitoring in Wikipedia thus contributing significantly to the CSCW community.
no code implementations • 17 Nov 2021 • Siddharth D Jaiswal, Karthikeya Duggirala, Abhisek Dash, Animesh Mukherjee
Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance.
no code implementations • 8 Feb 2022 • Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
While investigating for the fairness of the default action, we observe that over a set of as many as 1000 queries, in nearly 68% cases, there exist one or more products which are more relevant (as per Amazon's own desktop search results) than the product chosen by Alexa.
no code implementations • DravidianLangTech (ACL) 2022 • Mithun Das, Somnath Banerjee, Animesh Mukherjee
We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
1 code implementation • 1 Apr 2022 • Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure.
1 code implementation • Findings (NAACL) 2022 • Souvic Chakraborty, Parag Dutta, Sumegh Roychowdhury, Animesh Mukherjee
The last decade has witnessed a surge in the interaction of people through social networking platforms.
1 code implementation • 26 Apr 2022 • Mithun Das, Somnath Banerjee, Animesh Mukherjee
In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages.
1 code implementation • LREC 2022 • Mithun Das, Punyajoy Saha, Binny Mathew, Animesh Mukherjee
To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation.
1 code implementation • 9 May 2022 • Punyajoy Saha, Kanishk Singh, Adarsh Kumar, Binny Mathew, Animesh Mukherjee
We generate counterspeech using three datasets and observe significant improvement across different attribute scores.
1 code implementation • 28 Jun 2022 • Sayantan Adak, Altaf Ahmad, Aditya Basu, Animesh Mukherjee
A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form.
no code implementations • 1 Jul 2022 • Rima Hazra, Arpit Dwivedi, Animesh Mukherjee
Repositories of large software systems have become commonplace.
no code implementations • 5 Sep 2022 • Utkarsh Patel, Animesh Mukherjee, Mainack Mondal
Today, participating in discussions on online forums is extremely commonplace and these discussions have started rendering a strong influence on the overall opinion of online users.
1 code implementation • 7 Sep 2022 • Medidoddi Vahini, Jalend Bantupalli, Souvic Chakraborty, Animesh Mukherjee
Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems.
no code implementations • 21 Sep 2022 • Souvic Chakraborty, Pawan Goyal, Animesh Mukherjee
With the rising participation of the common mass in social media, it is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations.
1 code implementation • 7 Oct 2022 • Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee
To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets.
1 code implementation • 30 Nov 2022 • Punyajoy Saha, Divyanshu Sheth, Kushal Kedia, Binny Mathew, Animesh Mukherjee
We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains.
no code implementations • 11 Feb 2023 • Piush Aggarwal, Pranit Chawla, Mithun Das, Punyajoy Saha, Binny Mathew, Torsten Zesch, Animesh Mukherjee
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
1 code implementation • 27 Feb 2023 • Paramita Das, Sai Keerthana Karnam, Anirban Panda, Bhanu Prakash Reddy Guda, Soumya Sarkar, Animesh Mukherjee
With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases.
1 code implementation • 18 Mar 2023 • Punyajoy Saha, Kiran Garimella, Narla Komal Kalyan, Saurabh Kumar Pandey, Pauras Mangesh Meher, Binny Mathew, Animesh Mukherjee
Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community.
1 code implementation • 6 May 2023 • Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, Animesh Mukherjee
Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world.
no code implementations • 22 May 2023 • Mithun Das, Saurabh Kumar Pandey, Animesh Mukherjee
So far, several studies have been performed to develop robust hate speech detection systems.
no code implementations • 20 Jul 2023 • Anand Kumar Rai, Siddharth D Jaiswal, Animesh Mukherjee
Automatic speech recognition (ASR) systems are designed to transcribe spoken language into written text and find utility in a variety of applications including voice assistants and transcription services.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 10 Sep 2023 • Rima Hazra, Debanjan Saha, Amruit Sahoo, Somnath Banerjee, Animesh Mukherjee
To facilitate the task of the moderators, in this work, we have tackled two significant issues for the askubuntu CQA platform: (1) retrieval of duplicate questions given a new question and (2) duplicate question confirmation time prediction.
Community Question Answering Duplicate-Question Retrieval +1
no code implementations • 11 Sep 2023 • Abhilash Datta, Souvic Chakraborty, Animesh Mukherjee
We also perform a series of ablation studies to show how the baselines perform for our dataset.
no code implementations • 12 Sep 2023 • Rima Hazra, Agnik Saha, Somnath Banerjee, Animesh Mukherjee
Community Question Answering (CQA) platforms steadily gain popularity as they provide users with fast responses to their queries.
no code implementations • 19 Sep 2023 • Rima Hazra, Mayank Singh, Pawan Goyal, Bibhas Adhikari, Animesh Mukherjee
Interdisciplinarity has over the recent years have gained tremendous importance and has become one of the key ways of doing cutting edge research.
no code implementations • 9 Oct 2023 • Siddharth D Jaiswal, Ankit Kumar Verma, Animesh Mukherjee
Predictions for non-binary comments on all platforms are mostly female, thus propagating the societal bias that non-binary individuals are effeminate.
1 code implementation • 18 Oct 2023 • Mithun Das, Animesh Mukherjee
Finally, we perform a qualitative error analysis of the misclassified memes of the best-performing text-based, image-based and multimodal models.
no code implementations • 19 Oct 2023 • Sarthak Roy, Ashish Harshavardhan, Animesh Mukherjee, Punyajoy Saha
Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models.
no code implementations • 23 Jan 2024 • Somnath Banerjee, Amruit Sahoo, Sayan Layek, Avik Dutta, Rima Hazra, Animesh Mukherjee
In the continuously advancing AI landscape, crafting context-rich and meaningful responses via Large Language Models (LLMs) is essential.
no code implementations • 11 Feb 2024 • Mithun Das, Saurabh Kumar Pandey, Shivansh Sethi, Punyajoy Saha, Animesh Mukherjee
With the rise of online abuse, the NLP community has begun investigating the use of neural architectures to generate counterspeech that can "counter" the vicious tone of such abusive speech and dilute/ameliorate their rippling effect over the social network.
no code implementations • 19 Feb 2024 • Naquee Rizwan, Paramananda Bhaskar, Mithun Das, Swadhin Satyaprakash Majhi, Punyajoy Saha, Animesh Mukherjee
In this study, we aim to investigate the efficacy of these visual language models in handling intricate tasks such as hate meme detection.
1 code implementation • 20 Feb 2024 • Sayantan Adak, Daivik Agrawal, Animesh Mukherjee, Somak Aditya
We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs).
no code implementations • 21 Feb 2024 • Siddharth D Jaiswal, Ankit Kr. Verma, Animesh Mukherjee
Three of the commercial and five of the open-source FRSs are highly inaccurate; they further perpetuate biases against non-White individuals, with the lowest accuracy being 0%.
no code implementations • 22 Feb 2024 • Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha, Binny Mathew, Animesh Mukherjee
Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some existing method and need to be cross-checked (and corrected) by annotators to improve the model performance.
1 code implementation • 23 Feb 2024 • Somnath Banerjee, Sayan Layek, Rima Hazra, Animesh Mukherjee
We query a series of LLMs -- Llama-2-13b, Llama-2-7b, Mistral-V2 and Mistral 8X7B -- and ask them to generate both text and instruction-centric responses.
1 code implementation • 25 Feb 2024 • Somnath Banerjee, Avik Dutta, Aaditya Agrawal, Rima Hazra, Animesh Mukherjee
With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent.
no code implementations • 8 Mar 2024 • Arijit Nag, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
As means to reduce the number of tokens processed by the LLM, we consider code-mixing, translation, and transliteration of LRLs to HRLs.
1 code implementation • 22 Mar 2024 • Punyajoy Saha, Aalok Agrawal, Abhik Jana, Chris Biemann, Animesh Mukherjee
In terms of prompting, we find that our proposed strategies help in improving counter speech generation across all the models.
no code implementations • 27 Mar 2024 • Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Jens Frankenreiter, Stefan Bechtold, Krishna P. Gummadi
In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone's life.
no code implementations • CoNLL (EMNLP) 2021 • Arijit Nag, Bidisha Samanta, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English.