An improvement of pattern-based information extraction using intuitionistic fuzzy sets

Abstract

Multi-slot information extraction (IE) is a task that identify several related entities simultaneously. Most researches on this task are concerned with applying IE patterns (rules) to extract related entities from unstructured documents. An important obstacle for the success in this task is unknowingness where text portions containing interested information are. This problem is more complicated when involving languages with sentence boundary ambiguity, e.g. the Thai language. Applying IE rules to all reasonable text portions can degrade the effect of the obstacle, but it raises another problem that is incorrect (unwanted) extractions. This paper aims to present amethod for removing incorrect extractions. In the method, extractions are represented as intuitionistic fuzzy sets (IFSs), and a similarity measure for IFSs is used to calculate distance between IFS of an unclassified extraction and that of each already-classified extraction. The concept of k nearest neighbor is adopted to design whether the unclassified extraction is correct of not. From the preliminary experiment on a medical domain, the proposed technique improves extraction precision while satisfactorily preserving recall.