Knowledge Acquisition from a Collaboratively Generated Encyclopedia - Vol. 327 Dissertations in Artificial Intelligence

Knowledge Acquisition from a Collaboratively Generated Encyclopedia - Vol. 327 Dissertations in Artificial Intelligence

by S.P. Ponzetto
     
 

Research in Natural Language Processing (NLP) has made tremendous progress in the last two decades by employing data-driven techniques. However, further major advances can be achieved by integrating linguistic, domain and world knowledge into statistical approaches. In this dissertation, a methodology is presented to extract this knowledge from Wikipedia, a

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Overview

Research in Natural Language Processing (NLP) has made tremendous progress in the last two decades by employing data-driven techniques. However, further major advances can be achieved by integrating linguistic, domain and world knowledge into statistical approaches. In this dissertation, a methodology is presented to extract this knowledge from Wikipedia, a resource which has attracted the attention of many researchers in the Artificial Intelligence (AI) community, mainly because it provides semi-structured information and a large amount of manual annotations. The proposed approach uses the category system found in Wikipedia as a conceptual network. Semantic relations between categories are labeled to produce a large-scale taxonomy. This resource is evaluated by comparing it with Cyc and WordNet, as well as through computing semantic similarity between words and using semantic similarity measures as features for a state-of-the-art co-reference resolution system. The results show that this taxonomy can be successfully deployed for NLP tasks and represents a valuable semantic resource for AI applications.

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Product Details

ISBN-13:
9781607500971
Publisher:
I O S Press, Incorporated
Publication date:
02/15/2010
Pages:
236
Product dimensions:
5.90(w) x 8.20(h) x 0.60(d)

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