7 citations · 25 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Representation learning of drug and disease terms for drug repositioning
Sahil Manchanda, Ashish Anand
Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in cli…
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Abhishek, Ashish Anand, Amit Awekar
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate tr…