RELATION INFORMATION EXTRACTION USING DEEP SYNTACTIC ANALYSIS 深い統語解析を用いた関係情報抽出

ABSTRACT There has been an increasing need for natural language processing technology to Information Extraction (IE), such as relations between entities, which are more informative than mere documents searched by key words. This dissertation proposes a novel method to construct and utilize extraction patterns for relation extraction based on deep syntactic relations obtained by full parsing. The process which requires the most amount of manual work in construction of IE systems is construction of extraction patterns which extract target information from source texts, because the same information can be represented through many kinds of syntactic variations. To reduce this amount of manual work, our approach has two phases: First, we raise representation ability of extraction patterns and reduce number,of necessary patterns by normalizing syntactic variations into predicateargument structures (PASs) using a full parser based on Head-driven Phrase Structure Grammar (HPSG). Then, PASs which connect entities to extract in a small training corpus are considered as extraction patterns, and we divide them into components and utilize combinations of the components for generalization. As a real world application, we have constructed an IE system for protein-protein interactions, which are important knowledge in biomedical research. We evaluated the IE system on a small test-case corpus and a large real-world corpus, and show its effectiveness. This dissertation also describes aspects that should be considered to ensure effective-