A Cognitive-Based Approach to Identify Topics in Text Using the Web as a Knowledge Source
Louis Massey, Wilson Wong · IGI Global eBooks · 2011
This chapter explores the problem of topic identification from text. It is first argued that the conventional representation of text as bag-of-words vectors will always have limited success in arriving at the underlying meaning of text until the more fundamental issues of feature independence in vector-space and ambiguity of natural language are addressed. Next, a groundbreaking approach to text representation and topic identification that deviates radically from current techniques used for document classification, text clustering, and concept discovery is proposed. This approach is inspired by human cognition, which allows ‘meaning’ to emerge naturally from the activation and decay of unstructured text information retrieved from the Web. This paradigm shift allows for the exploitation rather than avoidance of dependence between terms to derive meaning without the complexity introduced by conventional natural language processing techniques. Using the unstructured texts in Web pages as a source of knowledge alleviates the laborious handcrafting of formal knowledge bases and ontologies that are required by many existing techniques. Some initial experiments have been conducted, and the results are presented in this chapter to illustrate the power of this new approach.