7 citations · 25 across the 10 of their papers we have counts for
11 papers
Taxonomical hierarchy of canonicalized relations from multiple Knowledge Bases
Akshay Parekh, Ashish Anand, Amit Awekar
This work addresses two important questions pertinent to Relation Extraction (RE). First, what are all possible relations that could exist between any two given entity types? Secon…
Unsupervised Representation Learning of DNA Sequences
Vishal Agarwal, N Jayanth Kumar Reddy, Ashish Anand
Recently several deep learning models have been used for DNA sequence based classification tasks. Often such tasks require long and variable length DNA sequences in the input. In t…
Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
Abhishek Abhishek, Sanya Bathla Taneja, Garima Malik +2
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and spo…
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…