Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/116678
Title: A graph-based approach to commonsense concept extraction and semantic similarity detection
Authors: Rajagopal, D.
Cambria, E. 
Olsher, D.
Kwok, K. 
Keywords: Ai
Commonsense knowledge representation and reasoning
Natural language processing
Semantic similarity
Issue Date: 2013
Source: Rajagopal, D.,Cambria, E.,Olsher, D.,Kwok, K. (2013). A graph-based approach to commonsense concept extraction and semantic similarity detection. WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web : 565-570. ScholarBank@NUS Repository.
Abstract: Commonsense knowledge representation and reasoning support a wide variety of potential applications in fields such as document auto-categorization, Web search enhancement, topic gisting, social process modeling, and concept-level opinion and sentiment analysis. Solutions to these problems, however, demand robust knowledge bases capable of supporting exible, nuanced reasoning. Populating such knowledge bases is highly time-consuming, making it necessary to develop techniques for deconstructing natural language texts into commonsense concepts. In this work, we propose an approach for effective multi-word commonsense expression extraction from unrestricted English text, in addition to a semantic similarity detection technique allowing additional matches to be found for specific concepts not already present in knowledge bases.
Source Title: WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web
URI: http://scholarbank.nus.edu.sg/handle/10635/116678
ISBN: 9781450320382
Appears in Collections:Staff Publications

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