Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking Algorithm for Semantic Document Annotations
International Journal of Information Retrieval Research
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Models of document indexing and document retrieval are mainly based on statistical NLP method. Computation of meaning is mainly based on the semantics. The former makes it possible to construct a high performance IR system easily. However, the latter is one of the significant methods which can substantially make computer well understand the language. The goal of this paper is to find the conjoined point which can combine the advantages of both schemes, and thus to propose an IR approach. Consequently, a concept-based IR model is proposed. This model is composed of two kernel schemes: the first is a domain language model, which is derived from the traditional language model. Its basic idea is to compute the conditional probability P(Q | D). The concept extracting approach, which is the second kernel scheme of the proposed model, originates from the traditional linguistics. It can help to well extract the meaning of a term. Thus, we can take the concept (the formalized meaning), instead of the lexical term, as the processing object in the proposed model, and consequently resolve the word sense ambiguity. Experiments on the TREC6 Chinese collection show that the proposed model outperforms the traditional TF-IDF methods, especially in the average precision and the overall search time.