activity
20162018
most citedSiamese Neural Networks with Random Forest for detecting duplicate question pairs

9 citations · 24 across the 7 of their papers we have counts for

collaborators

7 papers

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…

cs.CL20177 cited

Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

Patchigolla V S S Rahul, Sunil Kumar Sahu, Ashish Anand

Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event…

cs.CL2016

Recurrent neural network models for disease name recognition using domain invariant features

Sunil Kumar Sahu, Ashish Anand

Hand-crafted features based on linguistic and domain-knowledge play crucial role in determining the performance of disease name recognition systems. Such methods are further limite…