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components rather than a single Database table. So to minimise the time constraint, memory space and to do a smart search a new IR system is introduced. In the proposed system, searches can be divided into three categorise, namely (i) Main topic search (ii) Subtitle search and (iii) Keyword search. So the system would search
models such as a vector space model, a language model and two probabilistic models. We also proposed different measures to compute textual entailment between two terms allowing us to hopefully select appropriate keywords from thesauri to expand documents or queries automatically.
Topic tracking is to track trend of news topic, which people are interested in. It is a very pragmatic method in information retrieval. Compared with keywords retrieval, topic tracking excels in dynamic tracking based on text model and its content understanding, so it is mostly involved in text expressing and semantic
Information Retrieval (IR) methods are commonly based on words, these methods allow the user to formulate a query through keywords. However, there are situations where the user has only one example document and based on this example it is needed to recover the most similar documents in a collection. This paper
Automatic indexing is the foundation and core technology of automatic documents processing. Currently most of the documents don't have Keywords, and manual indexing consumes too much time and laborious, it is also highly subjective. This paper discusses the automatic indexing method on the calculation of a statistical
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
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