Concept Mining and Inner Relationship Discovery from Text

From the cognitive point of view, knowing concepts is a fundamental ability when human being understands the world. Most concepts can be lexicalized via words in a natural language and are called Lexical Concepts. Currently, there is much interest in knowledge acquisition from text automatically and in which concept extraction, verification, and relationship discovery are the crucial parts (Cao et al., 2002). There are a large range of other applications which can also be benefit from concept acquisition including information retrieval, text classification, and Web searching, etc. (Ramirez & Mattmann, 2004; Zhang et al., 2004; Acquemin & Bourigault, 2000) Most related efforts in concept mining are centralized in term recognition. The common used approaches are mainly based on linguistic rules (Chen et al., 2003), statistics (Zheng & Lu, 2005; Agirre et al., 2004) or a combination of both (Du et al., 2005; Velardi et al., 2001). In our research, we realize that concepts are not just terms. Terms are domain-specific while concepts are general-purpose. Furthermore, terms are just restricted to several kinds of concepts such as named entities. So even we can benefit a lot from term recognition we cannot use it to learn concepts directly. Other relevant works in concept mining are focused on concepts extraction from documents. Gelfand has developed a method based on the Semantic Relation Graph to extract concepts from a whole document (Gelfand et al., 1998). Nakata has described a method to index important concepts described in a collection of documents belonging to a group for sharing them (Nakata et al., 1998). A major difference between their works and ours is that we want to learn huge amount of concepts from a large-scale raw corpus efficiently rather than from one or several documents. So the analysis of documents will lead to a very higher time complexity and does not work for our purpose. There are many types relationships between lexical concepts such as antonymy, meronomy and hyponymy, among which the study of hyponymy relationship has attracted many effort of research because of its wide use. There are three mainstream approaches—the Symbolic approach, the Statistical approach and the Hierarchical approach—to discovery general 19