Book chapter
KXtractor: An Effective Biomedical Information Extraction Technique Based on Mixture Hidden Markov Models
Transactions on Computational Systems Biology II, pp 68-81
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a novel information extraction (IE) technique, KXtractor, which combines a text chunking technique and Mixture Hidden Markov Models (MiHMM). KXtractor overcomes the problem of the single Part-Of-Speech (POS) HMMs with modeling the rich representation of text where features overlap among state units such as word, line, sentence, and paragraph. KXtractor also resolves issues with the traditional HMMs for IE that operate only on the semi-structured data such as HTML documents and other text sources in which language grammar does not play a pivotal role. We compared KXtractor with three IE techniques: 1) RAPIER, an inductive learning-based machine learning system, 2) a Dictionary-based extraction system, and 3) single POS HMM. Our experiments showed that KXtractor outperforms these three IE systems in extracting protein-protein interactions. In our experiments, the F-measure for KXtractor was higher than for RAPIER, a dictionary-based system, and single POS HMM respectively by 16.89%, 16.28%, and 8.58%. In addition, both precision and recall of KXtractor are higher than those systems.
Metrics
Details
- Title
- KXtractor: An Effective Biomedical Information Extraction Technique Based on Mixture Hidden Markov Models
- Creators
- Min Song - Drexel UniversityIl-Yeol Song - Drexel UniversityXiaohua Hu - Drexel UniversityRobert B. Allen - Drexel University
- Publication Details
- Transactions on Computational Systems Biology II, pp 68-81
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000234378500005
- Scopus ID
- 2-s2.0-33646187984
- Other Identifier
- 991019170549804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods