Titlebook: Computing Meaning; Volume 4 Harry Bunt,Johan Bos,Stephen Pulman Book 2014 Springer Science+Business Media Dordrecht 2014 Computational sema

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Text, Speech and Language Technologyhttp://image.papertrans.cn/c/image/234735.jpg

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Theory of Statistical Experimentsto integrate it with uncertain, weighted knowledge, for example regarding word meaning. This paper describes a mapping between predicates of logical form and points in a vector space. This mapping is then used to project distributional inferences to inference rules in logical form. We then describe

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https://doi.org/10.1007/978-1-4613-8218-8he distributional meaning of the sentence is a function of the tensor products of the word vectors. Abstractly speaking, this function is the morphism corresponding to the grammatical structure of the sentence in the category of finite dimensional vector spaces. In this chapter, we provide a concret

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Games and Statistical Decisions,hallenge in natural language processing. One attempt to deal with this problem is combining deep semantic analysis and logical inference, as is done in the Nutcracker RTE system. In doing so, various obstacles will be met on the way: robust semantic analysis, designing interfaces to state-of-the-art

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Games and Statistical Decisions,the abductive inference procedure in a system called .. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and prod

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https://doi.org/10.1007/978-1-4613-8218-8rpretation. We extend past work in ., which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relatio