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

Neha Warikoo, Yung Chun Chang, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

1 引文 斯高帕斯(Scopus)

摘要

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 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.

原文英語
主出版物標題DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop
發行者Association for Computational Linguistics (ACL)
頁面57-64
頁數8
ISBN(電子)9781948087070
出版狀態已發佈 - 2017
事件1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 - Taipei, 臺灣
持續時間: 11月 27 2017 → …

出版系列

名字DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop

會議

會議1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017
國家/地區臺灣
城市Taipei
期間11/27/17 → …

ASJC Scopus subject areas

  • 軟體
  • 語言和語言學
  • 語言與語言學
  • 人工智慧

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