This publication is a suite of papers through prime researchers in computational semantics. It offers a state of the art assessment of contemporary and present learn in computational semantics, together with descriptions of recent equipment for developing and bettering assets for semantic computation, akin to WordNet, VerbNet, and semantically annotated corpora. It additionally provides new statistical equipment 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 common language processing, and the foremost to quite a lot of interesting functions. The breadth and intensity of insurance of this ebook makes it appropriate as a reference and evaluate of the kingdom 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 (Text, Speech and Language Technology, Volume 47)
Through application of higher order lambda functions). In our view, this leads to what are perhaps overly complex semantic representations of some basic linguistic constructions. In contrast, in the method we presented, these two concerns (meaning representation and semantic construction) are separated, enabling us to keep the semantics of constituents simple, while turning the construction of semantic expressions into a separate structured learning problem (with its own internal prediction and decoding mechanisms).
This will allow us to obtain the best of both situations: we will have the full expressivity of first-order logic and be able to reason with probabilities. We believe that this will allow for a more complete and robust approach to natural language understanding. While this is a large and complex task, this chapter proposes first steps toward our goal by presenting a mechanism for injecting distributional word-similarity information from a vector space into a first-order logical form. We define a mapping from predicate symbols of logical form to points in vector space.
Recently distributional models have been used to predict the applicability of paraphrases in context (Erk and Padó 2008; Thater et al. 2010; Reisinger and Mooney 2010; Dinu and Lapata 2010; 30 D. Garrette et al. Van de Cruys et al. 2011). For example, in “The spill left a stain”, result in is a better paraphrase for leave than flee, because of the context of spill and stain. In the sentence “The suspect left the country”, the opposite is true: flee is a better paraphrase. Usually, the distributional representation for a word mixes all its usages (senses).