Knowledge acquisition from a collaboratively generated encyclopedia

Research in Natural Language Processing (NLP) has made tremendous progress inthe 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

• ISBN: 978-1-60750-097-1
• Editorial: Ios Press
• Encuadernacion: Rústica
• Páginas: 236
• Fecha Publicación: 01/02/2010
• Nº Volúmenes: 1
• Idioma: Inglés