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The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and common-sense knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge. Multidimensional scaling is applied on the resulting knowledge base for open-domain opinion mining and sentiment analysis. © 2001-2011 IEEE.

Original publication

DOI

10.1109/MIS.2012.118

Type

Journal article

Journal

IEEE Intelligent Systems

Publication Date

01/01/2014

Volume

29

Pages

44 - 51