25 citations · 32 across the 9 of their papers we have counts for
11 papers · 1 filter
A Generative Approach for Financial Causality Extraction
Tapas Nayak, Soumya Sharma, Yash Butala +3
Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports. Each financial causality contains a cause span…
Sequence-Based Extractive Summarisation for Scientific Articles
Daniel Kershaw, Rob Koeling
This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the te…
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction
Rajdeep Mukherjee, Tapas Nayak, Yash Butala +2
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinio…
Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering
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 annotati…
A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
Tapas Nayak, Hwee Tou Ng
Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation…
RTE: A Tool for Annotating Relation Triplets from Text
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 te…