Information Extraction from Text – Dealing with Imprecise Data

Abstract

Fuzzy logic encompasses conceptual framework of sets and logic that is able to handle both precise and imprecise information and meaning. Although fuzzy systems still do not necessarily outperform human in dealing with uncertainty and imprecision, it helps to reduce the real world problems to a scale that is possible for computing solutions that was impossible before. The principal objective of the presented methods of this paper is to develop applications to enable information extraction under uncertainty, particularly on the conception and design of autonomous systems for natural language processing applications specifically on question and answering systems and textual entailment mechanism. A direct practical application of fuzzy logic to these fields does not seem to exist at present. Thus, higher order fuzzy system models based on fuzzy functions will have many uses in textual and semantic analysis, data mining, and search algorithms in the near future. In this paper, a new approach to information extraction via fuzzy functions is presented. The presented type-2 fuzzy inference system is used for uncertainty quantification of realworld data. Partitioning a given set of data into granules is most fundamental problem in pattern recognition and data mining. With the presented fuzzy inference system, we define membership functions based on the given dataset and use fuzzy clustering methods. The approach of membership function elicitation of type-2 fuzzy inference system is a category of hybrid knowledge-data class of fuzzy set elicitation and enables employment of different membership functions and local dependency function structures for each cluster. The major benefit of this approach is that, it does not require definition of membership function by an expert. The primary membership functions are found from fuzzy clustering methods presented in the paper and the secondary membership grades are optimized with genetic algorithms. The algorithm adopts simple type-reduction and does not require defuzzification. Textual entailment task is a challenging problem and depends on careful analysis of the features between the question and candidate answer pairs and an efficient classifier model such as uncertainty modeling tool presented in this paper