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In the Web 2.0 Age, Web bloggers have become major Web content providers, and the volume of blogs continues to increase rapidly. Although keyword-based tags are widely used to classify submitted blogs and to search for retrieval, this keyword-based approach provides less optimal classification, leading to less
Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combines keyword selection and
audiences and website's competitors when analyzing keywords; (2) insert keywords into web text that will appear on search engine results pages, and (3) involve their web content and websites with other web content creators. Implications: Because successful search engine optimization requires considerable time, professional
When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to the information retrieval systems (IRS) to obtain a precise representation of the user's information need and the context (preferences) of the information. To address this problem, we investigate
various applications. This paper presents a novel approach - Sparse Matrix Sparse Vector Multiplication (SpMSpV) to utilize sparse input vector efficiently. To demonstrate efficiency of the proposed algorithm, it has been applied to keyword based document search, where sparse matrix is used as index structure of text
In the area of Information Retrieval, user queries often mismatch the documents users exactly want. We regard this problem as a Query Rewriting task from user queries to document space. Using query logs containing query-keywords-CTR pairs, we trained a state-of-the-art statistical machine translation model to
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