Search Results for author: Tapas Nayak

Found 15 papers, 8 papers with code

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

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 Extraction

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 Extraction

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 Extraction

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

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.

Relation Extraction Word Embeddings

Effective Attention Modeling for Neural Relation Extraction

1 code implementation CONLL 2019 Tapas Nayak, Hwee Tou Ng

Relation extraction is the task of determining the relation between two entities in a sentence.

Relation Extraction

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

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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