Information extraction refers to the task of extracting relevant information from texts. This dissertation targets at extracting information of relations between biomedical concepts, which are explicitly represented with known linguistic structures in biomedical texts. Such structures of a target relation involve a keyword and its semantic arguments, where the keyword indicates the semantic type of the target relation and the arguments indicate the related concepts. The information of relations thus has two types of locality, such that the information is expressed in the local context of the keyword, called spatial locality, and that the keyword has well-known syntactic relations with its arguments, called structural locality. These two types of locality have been in the past handled by pattern matching and partial parsing approaches, respectively, but not at the same time. In this dissertation, we address this problem with a novel approach that searches for the arguments both bidirectionally and incrementally from the keywords.
The extraction process is divided into two steps. First, it uses a non-structured pattern that describes a context between a keyword and its arguments, to match an input sentence bidirectionally from the keyword. It then performs syntactic analysis incrementally on candidate arguments and, if necessary, on their sentential contexts as well, with a parser of a combinatory categorial grammar for rigorous syntactic verification of the candidates. The approach addresses the aforementioned spatial locality by utilizing non-structured patterns and the structural locality by employing a lazy evaluation parser that is customized for information extraction.
The approach is highly efficient, evidenced with experimental results, because it can stop the extraction process at any step, when the syntactic analysis gives a negative piece of evidence for extracting relevant information. We also show the applicabilit...