Relation extraction is an important task in biomedical areas such as protein-protein interaction, gene-disease interactions, and drugdisease interactions. In recent years, it has been widely researched to automatically extract biomedical relations in a vest amount of biomedical text data. In this paper, we propose a hybrid approach to extracting relations based on a rule-based approach feature set. We then use different classification algorithms such as SVM, Naïve Bayes, and Decision Tree classifiers for relation classification. The rationale for adopting shallow parsing and other NLP techniques to extract relations is two-folds: simplicity and robustness. We select seven features with the rule-based shallow parsing technique and evaluate the performance with four different PPI public corpora. Our experimental results show the stable performance in F-measure even with the relatively fewer features.
|Title of host publication||DTMBIO 2014 - Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics, co-located with CIKM 2014|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|Publication status||Published - 2014 Nov 7|
|Event||8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014 - Shanghai, China|
Duration: 2014 Nov 7 → …
|Name||DTMBIO 2014 - Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics, co-located with CIKM 2014|
|Other||8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014|
|Period||14/11/7 → …|
Bibliographical notePublisher Copyright:
© Copyright 2014 ACM.
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Science Applications