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.
IEEE Intelligent Systems
44 - 51