Excerpts

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. IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences Excerpted from Knowledge Acquisition from a Collaboratively Generated Encyclopedia - Volume 327 Dissertations in Artificial Intelligence All rights reserved by the original copyright owners. Excerpts are provided for display purposes only and may not be reproduced, reprinted or distributed without the written permission of the publisher.