65 citations · 121 across the 16 of their papers we have counts for
17 papers
MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction
Ankan Mullick, Akash Ghosh, G Sai Chaitanya +5
Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entit…
Adapting Pre-trained Generative Models for Extractive Question Answering
Prabir Mallick, Tapas Nayak, Indrajit Bhattacharya
Pre-trained Generative models such as BART, T5, etc. have gained prominence as a preferred method for text generation in various natural language processing tasks, including abstra…
Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?
Pratik Saini, Tapas Nayak, Indrajit Bhattacharya
Two distinct approaches have been proposed for relational triple extraction - pipeline and joint. Joint models, which capture interactions across triples, are the more recent devel…
FinRED: A Dataset for Relation Extraction in Financial Domain
Soumya Sharma, Tapas Nayak, Arusarka Bose +4
Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets. In the current literature, there is…
90% F1 Score in Relational Triple Extraction: Is it Real ?
Pratik Saini, Samiran Pal, Tapas Nayak +1
Extracting relational triples from text is a crucial task for constructing knowledge bases. Recent advancements in joint entity and relation extraction models have demonstrated rem…
Exploring Generative Models for Joint Attribute Value Extraction from Product Titles
Kalyani Roy, Tapas Nayak, Pawan Goyal
Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their…
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…
Aspect Sentiment Triplet Extraction Using Reinforcement Learning
Samson Yu Bai Jian, Tapas Nayak, Navonil Majumder +1
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expr…
Deep Neural Networks for Relation Extraction
Tapas Nayak
Relation extraction from text is an important task for automatic knowledge base population. In this thesis, we first propose a syntax-focused multi-factor attention network model f…
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
Tapas Nayak, Navonil Majumder, Pawan Goyal +1
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 ex…
Effective Attention Modeling for Neural Relation Extraction
Tapas Nayak, Hwee Tou Ng
Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be lon…
Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
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. There may be multiple relation tuples present in a te…