Download Computing Meaning: Volume 4 by Harry Bunt, Johan Bos, Stephen Pulman PDF

By Harry Bunt, Johan Bos, Stephen Pulman

This publication is a suite of papers through top researchers in computational semantics. It provides a state of the art assessment of contemporary and present learn in computational semantics, together with descriptions of latest equipment for developing and enhancing assets for semantic computation, resembling WordNet, VerbNet, and semantically annotated corpora. It additionally offers new statistical tools in semantic computation, corresponding to the applying of distributional semantics within the compositional calculation of sentence meanings. Computing the which means of sentences, texts, and spoken or texted discussion is the last word problem in traditional language processing, and the main to quite a lot of intriguing functions. The breadth and intensity of assurance of this e-book makes it appropriate as a reference and evaluate of the nation of the sphere for researchers in Computational Linguistics, Semantics, computing device technology, Cognitive technological know-how, and synthetic Intelligence. ​

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Extra resources for Computing Meaning: Volume 4

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48 D. Garrette et al. , & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical cooccurrence. Behavior Research Methods, Instruments, and Computers, 28, 203–208. , & Manning, C. D. (2009). An extended model of natural logic. In Proceedings of the eighth international conference on computational semantics (IWCS-8) (pp. 140–156). Miller, G. A. (2009). Wordnet—about us. edu. , & Lapata, M. (2008). Vector-based models of semantic composition. In Proceedings of ACL (pp. 236–244).

A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics 33 This definition of contextualization functions is similar to the framework of Mitchell and Lapata (2008), who define the meaning p of a two-word phrase p = vw as a function of the vectors for v and w, and their syntactic relation r in the text: p = f (v, w, r, K), where f is some function, and K is background knowledge. However, we use contextualization functions to compute the meaning of a word in context, rather than the meaning of a phrase.

91–98). Montague, R. (1974). The proper treatment of quantification in ordinary English. In R. ), Formal philosophy: Selected papers of Richard Montague (pp. 247–270). New Haven: Yale University Press. Nivre, J. (2003). An efficient algorithm for projective dependency parsing. In Proceedings of the 8th international workshop on parsing technologies, Nancy, France (pp. 149–160). , & Shieber, S. (1987). Prolog and natural language analysis. Stanford: CSLI. , & Domingos, P. (2009). Unsupervised semantic parsing.

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