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This paper proposes an extended vector space model (VSM), which is called M2VSM (meta keyword-based modified VSM). When conventional VSM is applied to document clustering, it is difficult to adjust the granularity of cluster in terms of topic. In order to solve the problem, M2VSM considers meta keywords such as
We consider topic detection without any prior knowledge of category structure or possible categories. Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with
This paper proposes a novel method to generate labels for grouping and organizing the search results returned by auxiliary search engines. It has applied statistical techniques to measure the quantities of co-occurrence keywords for forming the label matrix of them, and then agglomerated them into higher-level
The amount of multimedia information is rapidly increasing due to digital cameras and mobile telephones equipped with such devices. To interpret semantic of image, many researchers use keywords as textual annotation. However, current state of the art produces too many irrelevant keywords for images by annotator. They
addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA
The amount of information on the Web is growing at an exponential rate. Information overload has brought a heavy burden for modern life. Keyword based search engines no long fill the needs of many people. This paper introduces an approach towards intelligent information retrieval by providing clustered Web pages and
Since keyword-based search engine usually return large amount of results in which there are many unrelated documents and many documents with same content, automatic clustering technology is used to classify the retrieval results. While there are large amount of Web retrieval results, the clustering process usually
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
In the past few years, there has been an exponential increase in the amount of information available on the World Wide Web. This plethora of information can be extremely beneficial for users. However, the amount of human intervention that is currently required for this is inconvenient. Information extraction (IE) systems try to solve this problem by making the task as automatic as possible. Most of...
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
using keywords graph to contribute special techniques for exploring those groups and the relationships among them. Interactions between users and the created keywords graph are also provided. Compared to other applications on blog visualization, our approach utilized the ontology knowledge to analysis and automatically
preliminary flitting by clustering the incoming fax at first, and then uses Optical Character Recognition (OCR) technology to recognize the keywords and distinguish the junk fax. Proved by experiment, this method is not only easy to implement, but also available.
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