Computing Meaning, Volume 4 (Text, Speech and Language

This ebook is a suite of papers via major researchers in computational semantics. It provides a cutting-edge evaluate of contemporary and present learn in computational semantics, together with descriptions of recent equipment for developing and bettering assets for semantic computation, resembling WordNet, VerbNet, and semantically annotated corpora. It additionally offers new statistical equipment in semantic computation, reminiscent of the applying of distributional semantics within the compositional calculation of sentence meanings.

Computing the that means of sentences, texts, and spoken or texted discussion is the final word problem in traditional language processing, and the major to quite a lot of interesting purposes. The breadth and intensity of insurance of this e-book makes it compatible as a reference and review of the nation of the sphere for researchers in Computational Linguistics, Semantics, computing device technological know-how, Cognitive technological know-how, and synthetic Intelligence.

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Additional resources for Computing Meaning, Volume 4 (Text, Speech and Language Technology, Volume 47)

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For this reason, the function ζ can be used to limit the predicates Q considered for the right-hand sides of rules. If n ) = P n , then a rule will be generated for every Q ∈ P n . 2 Addressing Polysemy When a word is polysemous, this affects the applicability of vector space-based inference rules. Consider the rule ∀e[fix(e) → correct(e)] (any fixing event is a correcting event): this rule applies in contexts like “fix a problem”, but not in contexts like “fix the date”. We therefore need to take context into account when considering inference rule applicability.

Here, hypothesis h1 is a valid entailment, and should be judged to have high probability by the system. Hypothesis h2 should have lower probability since it uses the wrong sense of leave and h3 should be low probability because the logical operator not has reversed the meaning of the premise statement. While the most prominent forum using textual entailment is the Recognizing Textual Entailment (RTE) challenge (Dagan et al. 2005), the RTE datasets do not test the phenomena in which we are interested.

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. In Proceedings of the 2009 conference on empirical methods in natural language processing, Singapore (pp. 1–10). Stickel, M. (1985). Automated deduction by theory resolution.

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