Sunil Sahu


Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Rahul V S S Patchigolla | Sunil Sahu | Ashish Anand
BioNLP 2017

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 trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.

Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text
Desh Raj | Sunil Sahu | Ashish Anand
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

The task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health records. It is also a constraint for classifiers which employ manual feature engineering. In this paper, we propose a convolutional recurrent neural network (CRNN) architecture that combines RNNs and CNNs in sequence to solve this problem. The rationale behind our approach is that CNNs can effectively identify coarse-grained local features in a sentence, while RNNs are more suited for long-term dependencies. We compare our CRNN model with several baselines on two biomedical datasets, namely the i2b2-2010 clinical relation extraction challenge dataset, and the SemEval-2013 DDI extraction dataset. We also evaluate an attentive pooling technique and report its performance in comparison with the conventional max pooling method. Our results indicate that the proposed model achieves state-of-the-art performance on both datasets.


Recurrent neural network models for disease name recognition using domain invariant features
Sunil Sahu | Ashish Anand
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Relation extraction from clinical texts using domain invariant convolutional neural network
Sunil Sahu | Ashish Anand | Krishnadev Oruganty | Mahanandeeshwar Gattu
Proceedings of the 15th Workshop on Biomedical Natural Language Processing


Evaluating distributed word representations for capturing semantics of biomedical concepts
Muneeb TH | Sunil Sahu | Ashish Anand
Proceedings of BioNLP 15