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20162020
most citedSiamese Neural Networks with Random Forest for detecting duplicate question pairs

9 citations · 25 across the 8 of their papers we have counts for

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cs.CL2020

Relation Extraction with Self-determined Graph Convolutional Network

Sunil Kumar Sahu, Derek Thomas, Billy Chiu +2

Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in whi…

cs.CL20191 cited

Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa +1

Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing…

cs.CL20189 cited

Siamese Neural Networks with Random Forest for detecting duplicate question pairs

Ameya Godbole, Aman Dalmia, Sunil Kumar Sahu

Determining whether two given questions are semantically similar is a fairly challenging task given the different structures and forms that the questions can take. In this paper, w…

cs.CL20172 cited

Investigating how well contextual features are captured by bi-directional recurrent neural network models

Kushal Chawla, Sunil Kumar Sahu, Ashish Anand

Learning algorithms for natural language processing (NLP) tasks traditionally rely on manually defined relevant contextual features. On the other hand, neural network models using…

cs.CL20171 cited

What matters in a transferable neural network model for relation classification in the biomedical domain?

Sunil Kumar Sahu, Ashish Anand

Lack of sufficient labeled data often limits the applicability of advanced machine learning algorithms to real life problems. However efficient use of Transfer Learning (TL) has be…

cs.CL20174 cited

Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition

Sunil Kumar Sahu, Ashish Anand

Most existing methods for biomedical entity recognition task rely on explicit feature engineering where many features either are specific to a particular task or depends on output…