Search Results for author: Abhik Jana

Found 18 papers, 6 papers with code

Error Analysis of using BART for Multi-Document Summarization: A Study for English and German Language

1 code implementation NoDaLiDa 2021 Timo Johner, Abhik Jana, Chris Biemann

Recent research using pre-trained language models for multi-document summarization task lacks deep investigation of potential erroneous cases and their possible application on other languages.

Document Summarization Language Modelling +1

Enriching Hindi WordNet Using Knowledge Graph Completion Approach

1 code implementation EURALI (LREC) 2022 Sushil Awale, Abhik Jana

Even though the use of WordNet in the Natural Language Processing domain is unquestionable, creating and maintaining WordNet is a cumbersome job and it is even difficult for low resource languages like Hindi.

Knowledge Graph Completion

Towards Bengali WordNet Enrichment using Knowledge Graph Completion Techniques

no code implementations EURALI (LREC) 2022 Sree Bhattacharyya, Abhik Jana

Even though for languages like English, the existing WordNet is reasonably rich in terms of coverage, for resource-poor languages like Bengali, the WordNet is far from being reasonably sufficient in terms of coverage of vocabulary and relations between them.

Knowledge Graph Completion

Sentiment Analysis For Bengali Using Transformer Based Models

no code implementations ICON 2021 Anirban Bhowmick, Abhik Jana

Sentiment analysis is one of the key Natural Language Processing (NLP) tasks that has been attempted by researchers extensively for resource-rich languages like English.

Sentiment Analysis Sentiment Classification

CrowdCounter: A benchmark type-specific multi-target counterspeech dataset

1 code implementation2 Oct 2024 Punyajoy Saha, Abhilash Datta, Abhik Jana, Animesh Mukherjee

We evaluate two frameworks for generating counterspeech responses - vanilla and type-controlled prompts - across four large language models.

Diversity

On Zero-Shot Counterspeech Generation by LLMs

1 code implementation22 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.

Natural Language Processing in the Legal Domain

no code implementations23 Feb 2023 Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Bommarito II

To support our analysis, we construct and analyze a nearly complete corpus of more than six hundred NLP & Law related papers published over the past decade.

Using Distributional Thesaurus Embedding for Co-hyponymy Detection

no code implementations LREC 2020 Abhik Jana, Nikhil Reddy Varimalla, Pawan Goyal

Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community.

Binary Classification General Classification +1

Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs

1 code implementation IJCNLP 2019 Soumya Sharma, Bishal Santra, Abhik Jana, T. Y. S. S. Santosh, Niloy Ganguly, Pawan Goyal

Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model).

Knowledge Graphs

On the Compositionality Prediction of Noun Phrases using Poincar\'e Embeddings

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.

On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings

no code implementations7 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.

Detecting Reliable Novel Word Senses: A Network-Centric Approach

no code implementations14 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.

WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages

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.

Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?

no code implementations NAACL 2018 Abhik Jana, Pawan Goyal

), we turn a distributional thesaurus network into dense word vectors and investigate the usefulness of distributional thesaurus embedding in improving overall word representation.

Network Embedding Word Similarity

Network Features Based Co-hyponymy Detection

no code implementations LREC 2018 Abhik Jana, Pawan Goyal

Distinguishing lexical relations has been a long term pursuit in natural language processing (NLP) domain.

Relation

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