Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model

Neha Warikoo, Yung-Chun Chang, Wen-Lian Hsu

Abstract
In this work, we introduce a novel feature engineering approach named “algebraic invariance” to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed other compared approaches, achieved a high recall rate of 85.08%, and averaged an F1-score of 54.34% without the use of any additional knowledge bases.
Anthology ID:
W17-5809
Volume:
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
WS
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Publisher:
Association for Computational Linguistics
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Pages:
57–64
Language:
URL:
https://aclanthology.org/W17-5809
DOI:
Bibkey:
Cite (ACL):
Neha Warikoo, Yung-Chun Chang, and Wen-Lian Hsu. 2017. Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model. In Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pages 57–64, Taipei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model (Warikoo et al., 2017)
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