An Information Retrieval Model Based On Word Concept

摘要

Traditional approaches for information retrieval from texts depend on the term frequency. A shortcoming of these schemes, which consider only occurrences of the terms in a document, is that they have some limitations on extracting semantically exact indexes that represent the semantic content of a document. However, one word can always represent more than one meaning. The word sense ambiguities will also affect the system behavior. To address this issue, we proposed a brand new strategy - a concept extracting strategy to extract the concept of the word and to determine the semantic importance of the concepts in the sentences via analyzing the conceptual structures of the sentences. In this approach, a conceptual vector space model using auto-threshold detection is proposed to process the concepts, and a cluster searching model is also designed. This auto-threshold detection method can help the model to obtain the optimal settings of retrieval parameters automatically. An experiment on the TREC6 collection shows that the proposed method outperforms the other two information retrieval (IR) methods based on term frequency (TF), especially for the lower-ranked documents