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Web documents are heterogeneous and complex. There exists complicated associations within one web document and linking to the others. The high interactions between terms in documents demonstrate vague and ambiguous meanings. Efficient and effective clustering methods to discover latent and coherent meanings in context are necessary. This paper presents a fuzzy linguistic topological space along with...
In the last decade, Online Analytical Processing (OLAP) has taken an increasingly important role in Business Intelligence. Approaches, solutions and tools have been provided for both databases and data warehouses, which focus mainly on numerical data. These solutions are not suitable for textual data. Because of the fast growing of this type of data, there is a need for new approaches that take into...
In this paper, we propose a Type-2 Context-based Fuzzy C-Means (T2-CFCM) clustering algorithm and its linguistic model. This clustering technique builds information granules in the form of Type-2 fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The fundamental idea of conditional fuzzy clustering and Linguistic Model...
Semantic similarity of the words is used specially in searching operations. Although there are various searching tools available for the searching of words, texts, images etc. But sometimes exact matching of the word doesn't require but the similar words having same meaning is required. Hence an efficient algorithm is implemented for the searching of semantic similarity between words. Here an efficient...
In Korean, the pronunciations of phonemes are severely affected by their contexts. Thus, using phonemes directly translated from their written forms as basic units for acoustic modeling is problematic, as these units lack the ability to capture the complex pronunciation variations occurred in continuous speech. Allophone, a sub-phone unit in phonetics but served as independent phoneme in speech recognition,...
Information retrieval and extraction essentially rely on estimating the relevance of words present in a large corpus of documents or text. One of the approaches to measuring relevance is analyzing the importance of words based on their statistical distribution within a document. Quite another approach ensues from their linguistic relevance within a logically perceived context.
Pursuing on the analysis of product reviews, an unsupervised product features categorization method is proposed. Morphemes as smallest linguistic meaningful unit are induced in measuring the intra relationship among product features instead of words. Opinion words around product features are chosen to represent the inter relationship among product features instead of full context information. The...
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