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Text analysis of a web page is more difficult than the analysis of the text of normal document due to the presence of additional information, such as HTML structure, styling codes, irrelevant text, and presence of hyperlinks. In this paper, we propose an unsupervised method to extract keywords from a web page. The
In this paper, we propose a novel image search scheme is contextual image search with keyword input. It is different from conventional image search schemes. it consist of three step process, first one is context extraction to distinguish the image entities of the same name, second step is conceptualization to convert
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
Web 2.0 tools and environments have made tagging, the act of assigning keywords to on-line objects, a popular way to annotate shared resources. The success of now-prominent tagging systems makes tagging "the natural way for people to classify objects as well as an attractive way to discover new material". One of the
users to shift through and find relevant information. The information retrievals commonly used are based on keywords. These techniques used keyword lists to describe the content of information, but one problem with such list is that they do not say anything about the symantic relationships between keywords, nor do they
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
keyword specified by the investigator or suggested by system. Experiments were conducted on dummy crime dataset to test the accuracy and the scalability of the proposed system. Experimental results proved that subject suggestion improved the accuracy and thus speeds up the process of searching the evidence.
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
This paper presents the summary of experience obtained with the modified clustering algorithm of Projective Adaptive Resonance Theory. The algorithm was proposed by authors, and was tested for text processing. Possible usage of the algorithm is exemplified by text document clustering, and generation of keyword
In this paper, we examine the significance of expansion of the user query by two techniques namely Efficient Clustering-By-Direction and Theme Clustering. These two techniques produce the clusters of keywords extracted from the set of retrieved documents for the user query. The former clustering is based on
as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features
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