A large margin approach to anaphora resolution for neuroscience knowledge discovery
|A large margin approach to anaphora resolution for neuroscience knowledge discovery|
|Author(s)||Burak Ozyurt I.|
|Published in||Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22|
|Keyword(s)||Unknown (Extra: Anaphora resolution, Baseline methods, Knowledge Discovery, Large margin classifiers, Probabilistic output, Semantic features, Wikipedia, Word Sense Disambiguation, Wordnet, Artificial intelligence, Classifiers, Semantics, Support vector machines, Image retrieval)|
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A large margin approach to anaphora resolution for neuroscience knowledge discovery is a 2009 conference paper written in English by Burak Ozyurt I. and published in Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22.
A discriminative large margin classifier based approach to anaphora resolution for neuroscience abstracts is presented. The system employs both syntactic and semantic features. A support vector machine based word sense disambiguation method combining evidence from three methods, that use WordNet and Wikipedia, is also introduced and used for semantic features. The support vector machine anaphora resolution classifier with probabilistic outputs achieved almost four-fold improvement in accuracy over the baseline method. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved.
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