Please use this identifier to cite or link to this item:
|Title:||Feature generation and representations for protein-protein interaction classification|
|Keywords:||Biomedical text classification|
|Citation:||Lan, M., Tan, C.L., Su, J. (2009). Feature generation and representations for protein-protein interaction classification. Journal of Biomedical Informatics 42 (5) : 866-872. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jbi.2009.07.004|
|Abstract:||Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively. © 2009 Elsevier Inc. All rights reserved.|
|Source Title:||Journal of Biomedical Informatics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 16, 2019
WEB OF SCIENCETM
checked on Jan 1, 2019
checked on Dec 16, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.