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…