Vipin Chaudhary


2019

pdf bib
Attention Neural Model for Temporal Relation Extraction
Sijia Liu | Liwei Wang | Vipin Chaudhary | Hongfang Liu
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction.

2017

pdf bib
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
Sijia Liu | Feichen Shen | Vipin Chaudhary | Hongfang Liu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we present MayoNLP’s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection.