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 the common sense knowledge humans normally acquire during the formative years of their lives. If we want machines to really understand natural language, we need to provide them with this kind of knowledge rather than relying on the valence of keywords and word co-occurrence frequencies. In this work, we blend the largest existing taxonomy of common knowledge with a natural-language-based semantic network of common sense knowledge, and use multi-dimensionality reduction techniques on the resulting knowledge base for opinion mining and sentiment analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 the common sense knowledge humans normally acquire during the formative years of their lives. 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