If NLP systems could better simulate how people would evaluate various states of the world in contexts of interest, this would make it easier to accurately extract embedded sentiments and avoid being led astray by solely linguistic cues. If this knowledge could then be combined with 'fullsemantics' linguistic processing capable of modeling the interplay between lexical and syntactic semantics and then interweaving these with domain knowledge, this would allow the use of important semantic information (including argument and, especially, valence structure) implicit in phrases such as 'Critics say' and 'Despite this.' The present paper seeks to implement these insights, employing domain models grounded in the INTELNET/COGVIEW 'energy-based' knowledge representation formalism and the Radical Construction Grammar-based COGPARSE parser, bringing together concepts, knowledge, language processing, and opinion mining.
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机译：如果NLP系统可以更好地模拟人们在感兴趣的上下文中如何评估世界的各种状况，那么这将使更容易准确地提取嵌入的情感，并避免仅因语言暗示而误入歧途。如果然后可以将该知识与能够对词汇语义和句法语义之间的相互作用进行建模的“全语义”语言处理相结合，然后将它们与领域知识交织在一起，则可以使用重要的语义信息（包括论点，尤其是价结构）隐含在诸如“评论家说”和“尽管如此”之类的短语中。本文力图利用基于INTELNET / COGVIEW“基于能量”的知识表示形式主义和基于Radical Construction Grammar的COGPARSE解析器的领域模型来实现这些见解，将概念，知识，语言处理和意见挖掘结合在一起。
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