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By Yaakov HaCohen-Kerner, Aharon Tayeb (auth.), Allan Ramsay, Gennady Agre (eds.)

This publication constitutes the refereed court cases of the fifteenth overseas convention on man made Intelligence: method, structures, and purposes, AIMSA 2012, held in Varna, Bulgaria in September 2012.
The 36 revised complete papers awarded have been rigorously reviewed and chosen from various submissions. The papers are geared up in topical sections on ordinary language processing, social networks, wisdom illustration and reasoning, computer studying, making plans and brokers, seek, and desktop vision.

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Extra resources for Artificial Intelligence: Methodology, Systems, and Applications: 15th International Conference, AIMSA 2012, Varna, Bulgaria, September 12-15, 2012. Proceedings

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LAAA is focused on supporting linguists, recall is important, so up to maxatt activation areas are finally returned as suggested attachment areas. However, some lemmas can be highly polysemous and we can have good evidence for their senses. e. based on reliable evidence, and several KS. 4 Experiment Setup We follow evaluation methodology proposed in [1]: first we remove a word sample from a wordnet, then we apply expansion algorithm to reattach the words. Only words above a corpus frequency threshold are included in a sample (MSR extraction methods work well for more frequent words) – 200 was used.

PWE returned propositions for more words than LAAA. 3%). As both algorithms are designed for expansion of existing wordnets, the slight decrease in coverage is not as important as the increase in sense recall of the algorithm. 6 Conclusions We presented a new wordnet expansion algorithm called Lexical Area Attachment Algorithm based on an idea of combining different heterogeneous KSs and correcting their errors by expanding their support across local wordnet subgraphs. LAAA can use any KS, as it does not assume the probabilistic character of KSs.

E. g. for each hypernymic pair of synsets all LU pairs made from it are linked by hypernymy in B . Relations from B do not include any LU pairs that are not supported by the synset relations from B. A knowledge source K is a set of triples of the type: L × L × R where R is a set of real numbers. Let K is a matrix of the KSs and W is a matrix of the global weights assigned to the KSs (local weights are stored in the triples). 2 Algorithm Algorithm consists of two phases. During the first lemma-to-synset semantic fit for a new lemma x is calculated by combing direct support from the synset lemmas and indirect support from the synset context.

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