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Does there exist a compact set of visual topics in form of keyword clusters capable to represent all images visual content within an acceptable error? In this paper, we answer this question by analyzing distribution laws for keywords from image descriptions and comparing with traditional techniques in NLP, thereby
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
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
In this paper we propose an approach for Chinese question analysis and answer extraction. A general question analysis process contains keyword extraction and question classification. Question classification plays a crucial role in automatic question answering. To implement the question classification, we have carried
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
Semantic-based information retrieval mechanism that handles the processing, recognition, extraction, extensions and matching of content semantics to achieve the following objectives: i) to analyze and determine the semantic feature of the content and to develop a semantic pattern that represent the semantic features of the content. ii) to analyze user's query and extend its implied semantics through...
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
A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We
methodology. This is performed to explore the emotional structure that forms when user interacts with the product. An evaluation using 20 cell-phones as stimuli, a set of 40 emotional keywords, and 30 subjects were used. Principal Component Analysis (PCA) and cluster analysis were performed to analyze the distribution of
analyzer to pick up information of service and use keywords to find out related services; then we cluster Web services according to the similarity of services; last, we select the appropriate Web service from list of services.
a kernel-selected algorithm based on the lowest similarity, afterwards we get the appropriate keywords to label the topic of each cluster. Finally, experiments on 20Newsgruops email dataset show the validity of our approach and the experimental results also well match the labeled human clustering result.
With the increasing amount of unstructured content available electronically on the web, content categorization becomes very important for efficient information retrieval. The basic approaches for information retrieval in text documents are searching using keywords, categorization of the documents and filtering out the
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