Document Indexing Using Independent Topic Extraction

Text retrieval involves finding relevant information from a collection of documents given the user’s information need. Traditional information retrieval systems represent a document as a vector of words, where each component can be a simple word count or follows a more sophisticated weighting scheme. The composed matrix is usually sparse and some words are highly correlated with each other. Another representation scheme is focused on the underlying topics which is usually obtained by a variety of dimension reduction techniques, where each document can be represented as a vector of topic intensity. In this paper, We proposed a novel indexing technique based on independent component analysis. From the experiments performed on AP news articles, the performance improvements is significant when the topic of query is closely related to the topics which is extracted from the ICA.