Tirthankar Ghosal


2021

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INNOVATORS at SemEval-2021 Task-11: A Dependency Parsing and BERT-based model for Extracting Contribution Knowledge from Scientific Papers
Hardik Arora | Tirthankar Ghosal | Sandeep Kumar | Suraj Patwal | Phil Gooch
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

In this work, we describe our system submission to the SemEval 2021 Task 11: NLP Contribution Graph Challenge. We attempt all the three sub-tasks in the challenge and report our results. Subtask 1 aims to identify the contributing sentences in a given publication. Subtask 2 follows from Subtask 1 to extract the scientific term and predicate phrases from the identified contributing sentences. The final Subtask 3 entails extracting triples (subject, predicate, object) from the phrases and categorizing them under one or more defined information units. With the NLPContributionGraph Shared Task, the organizers formalized the building of a scholarly contributions-focused graph over NLP scholarly articles as an automated task. Our approaches include a BERT-based classification model for identifying the contributing sentences in a research publication, a rule-based dependency parsing for phrase extraction, followed by a CNN-based model for information units classification, and a set of rules for triples extraction. The quantitative results show that we obtain the 5th, 5th, and 7th rank respectively in three evaluation phases. We make our codes available at https://github.com/HardikArora17/SemEval-2021-INNOVATORS.

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Proceedings of the Fifth Workshop on Widening Natural Language Processing
Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Kyathi Chandu | Xanda Schofield | Surangika Ranathunga | Haley Lepp | Tirthankar Ghosal
Proceedings of the Fifth Workshop on Widening Natural Language Processing

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Proceedings of the Second Workshop on Scholarly Document Processing
Iz Beltagy | Arman Cohan | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Keith Hall | Drahomira Herrmannova | Petr Knoth | Kyle Lo | Philipp Mayr | Robert M. Patton | Michal Shmueli-Scheuer | Anita de Waard | Kuansan Wang | Lucy Lu Wang
Proceedings of the Second Workshop on Scholarly Document Processing

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Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead
Khalid Al Khatib | Tirthankar Ghosal | Yufang Hou | Anita de Waard | Dayne Freitag
Proceedings of the Second Workshop on Scholarly Document Processing

Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.

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IITP-CUNI@3C: Supervised Approaches for Citation Classification (Task A) and Citation Significance Detection (Task B)
Kamal Kaushik Varanasi | Tirthankar Ghosal | Piyush Tiwary | Muskaan Singh
Proceedings of the Second Workshop on Scholarly Document Processing

Citations are crucial to a scientific discourse. Besides providing additional contexts to research papers, citations act as trackers of the direction of research in a field and as an important measure in understanding the impact of a research publication. With the rapid growth in research publications, automated solutions for identifying the purpose and influence of citations are becoming very important. The 3C Citation Context Classification Task organized as part of the Second Workshop on Scholarly Document Processing @ NAACL 2021 is a shared task to address the aforementioned problems. In this paper, we present our team, IITP-CUNI@3C’s submission to the 3C shared tasks. For Task A, citation context purpose classification, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the relation between the citation context and the cited paper for citation classification. For Task B, citation context influence classification, we use a set of simple features to classify citations based on their perceived significance. We achieve comparable performance with respect to the best performing systems in Task A and superseded the majority baseline in Task B with very simple features.

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Overview of the Second Workshop on Scholarly Document Processing
Iz Beltagy | Arman Cohan | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Keith Hall | Drahomira Herrmannova | Petr Knoth | Kyle Lo | Philipp Mayr | Robert Patton | Michal Shmueli-Scheuer | Anita de Waard | Kuansan Wang | Lucy Wang
Proceedings of the Second Workshop on Scholarly Document Processing

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

2020

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Proceedings of the First Workshop on Scholarly Document Processing
Muthu Kumar Chandrasekaran | Anita de Waard | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Petr Knoth | David Konopnicki | Philipp Mayr | Robert M. Patton | Michal Shmueli-Scheuer
Proceedings of the First Workshop on Scholarly Document Processing

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Overview of the First Workshop on Scholarly Document Processing (SDP)
Muthu Kumar Chandrasekaran | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Philipp Mayr | Michal Shmueli-Scheuer | Anita de Waard
Proceedings of the First Workshop on Scholarly Document Processing

Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. 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. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website: https://ornlcda.github.io/SDProc

2019

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DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions
Tirthankar Ghosal | Rajeev Verma | Asif Ekbal | Pushpak Bhattacharyya
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticised sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. 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. Here in this work, we investigate the role of reviewer sentiment embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and review’s polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (∼ 29% error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence, especially when non-responding/missing reviewers are frequent in present day peer review.

2018

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TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
Tirthankar Ghosal | Amitra Salam | Swati Tiwari | Asif Ekbal | Pushpak Bhattacharyya
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
Tirthankar Ghosal | Vignesh Edithal | Asif Ekbal | Pushpak Bhattacharyya | George Tsatsaronis | Srinivasa Satya Sameer Kumar Chivukula
Proceedings of the 27th International Conference on Computational Linguistics

The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Networks (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and do not require any manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation which we coin as the Relative Document Vector (RDV). The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5% in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where paraphrased passages closely resemble to semantically redundant documents.

2017

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Document Level Novelty Detection: Textual Entailment Lends a Helping Hand
Tanik Saikh | Tirthankar Ghosal | Asif Ekbal | Pushpak Bhattacharyya
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)