Search Results for author: Tapas Nayak

Found 23 papers, 10 papers with code

MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction

no code implementations18 Jan 2024 Ankan Mullick, Akash Ghosh, G Sai Chaitanya, Samir Ghui, Tapas Nayak, Seung-Cheol Lee, Satadeep Bhattacharjee, Pawan Goyal

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc.

Relation Relation Extraction

tagE: Enabling an Embodied Agent to Understand Human Instructions

1 code implementation24 Oct 2023 Chayan Sarkar, Avik Mitra, Pradip Pramanick, Tapas Nayak

At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language.

Natural Language Understanding Question Answering +1

Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?

no code implementations1 Oct 2023 Pratik Saini, Tapas Nayak, Indrajit Bhattacharya

Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks.

Relation Relation Extraction +1

FinRED: A Dataset for Relation Extraction in Financial Domain

1 code implementation6 Jun 2023 Soumya Sharma, Tapas Nayak, Arusarka Bose, Ajay Kumar Meena, Koustuv Dasgupta, Niloy Ganguly, Pawan Goyal

Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets.

Financial Relation Extraction Relation +1

90% F1 Score in Relational Triple Extraction: Is it Real ?

no code implementations20 Feb 2023 Pratik Saini, Samiran Pal, Tapas Nayak, Indrajit Bhattacharya

This approach leads to overall performance improvement in these models within the realistic experimental setting.

Joint Entity and Relation Extraction Relation +1

A Generative Approach for Financial Causality Extraction

1 code implementation12 Apr 2022 Tapas Nayak, Soumya Sharma, Yash Butala, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly

Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports.

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

1 code implementation EMNLP 2021 Rajdeep Mukherjee, Tapas Nayak, Yash Butala, Sourangshu Bhattacharya, Pawan Goyal

Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.

Aspect Sentiment Triplet Extraction Sentence

Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering

1 code implementation RANLP 2021 Tapas Nayak, Navonil Majumder, Soujanya Poria

Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.

Relation Relation Extraction +1

A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents

1 code implementation RANLP 2021 Tapas Nayak, Hwee Tou Ng

Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations.

Relation Relation Extraction +1

RTE: A Tool for Annotating Relation Triplets from Text

no code implementations18 Aug 2021 Ankan Mullick, Animesh Bera, Tapas Nayak

In this work, we present a Web-based annotation tool `Relation Triplets Extractor' \footnote{https://abera87. github. io/annotate/} (RTE) for annotating relation triplets from the text.

Relation Relation Extraction +1

Deep Neural Networks for Relation Extraction

1 code implementation5 Apr 2021 Tapas Nayak

Relation extraction from text is an important task for automatic knowledge base population.

Joint Entity and Relation Extraction Knowledge Base Population +1

Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey

no code implementations31 Mar 2021 Tapas Nayak, Navonil Majumder, Pawan Goyal, Soujanya Poria

Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very difficult to keep track of so many papers.

Document-level Relation Extraction Relation +2

Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction

1 code implementation22 Nov 2019 Tapas Nayak, Hwee Tou Ng

A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text.

Joint Entity and Relation Extraction Machine Translation +2

CATaLog Online: Porting a Post-editing Tool to the Web

no code implementations LREC 2016 Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Tapas Nayak, Mihaela Vela, Josef van Genabith

The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper.

Machine Translation Management +1

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