33 citations · 58 across the 16 of their papers we have counts for
16 papers
VQA with Cascade of Self- and Co-Attention Blocks
Aakansha Mishra, Ashish Anand, Prithwijit Guha
The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation throug…
Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy
Akshay Parekh, Ashish Anand, Amit Awekar
Large crowd-sourced datasets are often noisy and relation classification (RC) datasets are no exception. Reannotating the entire dataset is one probable solution however it is not…
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages
Akshara Prabhakar, Gouri Sankar Majumder, Ashish Anand
Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean trai…
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…
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…
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
Sunil Kumar Sahu, Ashish Anand
Simultaneous administration of multiple drugs can have synergistic or antagonistic effects as one drug can affect activities of other drugs. Synergistic effects lead to improved th…
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…
Relation extraction from clinical texts using domain invariant convolutional neural network
Sunil Kumar Sahu, Ashish Anand, Krishnadev Oruganty +1
In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject…
Inferring disease correlation from healthcare data
Gargi Priyadarshini, Ashish Anand
Electronic Health Records maintained in health care settings are a potential source of substantial clinical knowledge. The massive volume of data, unstructured nature of records an…