Download e-book for iPad: Current Topics in Artificial Intelligence: 12th Conference by Daniel Borrajo, Luis Castillo, Juan Manuel Corchado

By Daniel Borrajo, Luis Castillo, Juan Manuel Corchado

ISBN-10: 2007938154

ISBN-13: 9782007938159

ISBN-10: 3540752706

ISBN-13: 9783540752707

This publication constitutes the refereed lawsuits of the twelfth convention of the Spanish organization for man made Intelligence, CAEPIA 2007, held in Salamanca, Spain, in November 2007, along with the seventh Workshop on man made Intelligence expertise move, TTIA 2007.

The 28 revised complete papers offered have been conscientiously chosen in the course of rounds of reviewing and development from 134 submissions. The papers handle all present problems with man made intelligence starting from methodological and foundational features to complicated functions in a number of fields.

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Extra info for Current Topics in Artificial Intelligence: 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2007, Salamanca, Spain, November ...

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Clustering-based approach for retrieving relevant concepts for the word co-occurrence W Require: A word co-occurrence W . Ensure: The set of retrieved concepts S. Let SW be the set of all senses of words in W , and PW be the set of all WordNet multi-word concepts possibly related to W . S = SW ∪ PW i=0 repeat i = i+1 G = clustering(S, β0 (i)) G = filter (G, W ) S = ∪ {s|s ∈ g} g∈G until |S|W = |W | or β0 (i + 1) = 1 Remove from S those senses corresponding to words that compose multi-word concepts appearing in S.

M. ): CAEPIA 2007, LNAI 4788, pp. 21–30, 2007. c Springer-Verlag Berlin Heidelberg 2007 22 H. Anaya-S´ anchez, R. Berlanga-Llavori, and A. Pons-Porrata crucial task for the development of the Semantic Web consists of annotating web pages with concepts from domain ontologies. To the best of our knowledge, there not exists any general and domain-independent approach for concept retrieval. Instead, several specific annotation tools have been developed for specific domains. For example, METAMAP2 permits the retrieval of concepts from UMLS.

S = SW ∪ PW i=0 repeat i = i+1 G = clustering(S, β0 (i)) G = filter (G, W ) S = ∪ {s|s ∈ g} g∈G until |S|W = |W | or β0 (i + 1) = 1 Remove from S those senses corresponding to words that compose multi-word concepts appearing in S. return S Retrieval of Relevant Concepts from a Text Collection 25 Sense Representation. For clustering purposes, word senses are represented as topic signatures [6]. Thus, for each word sense s we define a vector (t1 : σ1 , . . , tm : σm ), where each ti is a WordNet term highly correlated to s with an association weight σi .

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Current Topics in Artificial Intelligence: 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2007, Salamanca, Spain, November ... by Daniel Borrajo, Luis Castillo, Juan Manuel Corchado


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