Computing Meaning, Volume 4 (Text, Speech and Language

This e-book is a set of papers by way of major researchers in computational semantics. It offers a cutting-edge assessment of contemporary and present learn in computational semantics, together with descriptions of latest tools for developing and enhancing assets for semantic computation, reminiscent of WordNet, VerbNet, and semantically annotated corpora. It additionally provides new statistical tools in semantic computation, equivalent 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 average language processing, and the main to quite a lot of fascinating purposes. The breadth and intensity of assurance of this booklet makes it compatible as a reference and assessment of the kingdom of the sector for researchers in Computational Linguistics, Semantics, laptop technology, 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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Given a vector space V and a finite set R of semantic relations, the set C(V , R) of contexts over V and R consists of all finite sets of pairs from V × R. That is, we describe the context in which a target word occurs as a finite set of pairs (⃗ v , r) of a context item v⃗ represented as a point in vector space, and the relation r between the context item and the target. For a word w in a context c ∈ C(V , R), the context-specific meaning w ⃗ c of w is a function of the out-of-context vector w ⃗ for w and the context c: ⃗ c) w ⃗ c = α(w, The function α is a contextualization function with signature α : V × C(V , R) → V .

Their dimensions are qualities, like the hue and saturation of a color or the taste of a fruit. Points in a conceptual space are, therefore, potential entities. In contrast, the vector spaces that we use are distributional in nature, and, therefore, cannot be interpreted as potential entities. A point in such a space is a potential word, defined through its observed contexts. For this reason, we define the link between logical form and vector space through a second mapping function independent of the interpretation function, which we call the lexical mapping function.

Stickel, M. (1985). Automated deduction by theory resolution. Journal of Automated Reasoning, 1, 333–355. van Benthem, J. (1986). Essays in logical semantics. Dordrecht: Reidel. van Benthem, J. (1995). Language in action: Categories, lambdas, and dynamic logic. Cambridge: MIT Press. , & Matsumoto, Y. (2003). Statistical dependency analysis with support vector machines. In Proceedings of the 8th international workshop on parsing technologies, Nancy, France (pp. 195–206). Zettlemoyer, L. , & Collins, M.

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