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.
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
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• Nanotechnology
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• All aspects of physics
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• E-commerce
• The knowledge economy
• Urban studies
• Arms control
• Understanding and responding to terrorism
• Medical informatics
• Computer Sciences
Synopsis
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