Search Results for author: Tirthankar Ghosal

Found 18 papers, 8 papers with code

Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead

no code implementations NAACL (sdp) 2021 Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, Dayne Freitag

Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication.

Argument Mining

Overview of the First Workshop on Scholarly Document Processing (SDP)

no code implementations EMNLP (sdp) 2020 Muthu Kumar Chandrasekaran, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard

To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event.

Novelty Detection: A Perspective from Natural Language Processing

no code implementations CL (ACL) 2022 Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, Pushpak Bhattacharyya

In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem.

Natural Language Inference Novelty Detection

When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews

1 code implementation28 Oct 2023 Sandeep Kumar, Tirthankar Ghosal, Asif Ekbal

To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically.

RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

1 code implementation5 Feb 2022 Ashish Rana, Deepanshu Khanna, Tirthankar Ghosal, Muskaan Singh, Harpreet Singh, Prashant Singh Rana

Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim.

Benchmarking Binary Classification +3

Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection

1 code implementation15 Nov 2021 Jan Philip Wahle, Nischal Ashok, Terry Ruas, Norman Meuschke, Tirthankar Ghosal, Bela Gipp

We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.


DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions

1 code implementation ACL 2019 Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, Pushpak Bhattacharyya

However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration.

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

2 code implementations LREC 2018 Tirthankar Ghosal, Amitra Salam, Swati Tiwari, Asif Ekbal, Pushpak Bhattacharyya

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.

Benchmarking Document Summarization +4

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