Extracting Chemical Information from Thai Unstructured Text with Unknown Phrase Boundaries

Due to the limitations of language-processing tools for the Thai language, pattern-based information extraction from Thai documents requires supplementary techniques. Based on sliding-window rule application and extraction filtering, we present a framework for extracting multi-slot frames describing chemical reactions and those describing chemical syntheses from Thai unstructured 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. The experimental results show that the filtering components improve precision while preserving recall satisfactorily.