Extracting chemical reactions from Thai text for semantics-based information retrieval

Authors:
Peerasak Intarapaiboon;Ekawit Nantajeewarawat;Thanaruk Theeramunkong
Affiliations:
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand;School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand;School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Year:
2010

Citing 3
Cited 0

Learning Information Extraction Rules for Semi-Structured and Free Text

Machine Learning - Special issue on natural language learning
Machine Learning for Information Extraction in Informal Domains

Machine Learning - Special issue on information retrieval
Bottom-up relational learning of pattern matching rules for information extraction

The Journal of Machine Learning Research

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Abstract

Based on sliding-window rule application and extraction filtering, we present a framework for extracting multi-slot frames describing chemical reactions from Thai free text with unknown target-phrase boundaries. A supervised rule learning algorithm is employed for automatic construction of pattern-based extraction rules from hand-tagged training phrases. A filtering method is devised for removal of incorrect extraction results based on features observed from text portions appearing between adjacent slot fillers in source documents. Extracted reaction frames are represented as concept expressions in description logics and are used as metadata for document indexing. A document knowledge base supporting semantics-based information retrieval is constructed by integrating document metadata with domain-specific ontologies.