By Harry Bunt, Johan Bos, Stephen Pulman
This booklet is a set of papers by means of prime researchers in computational semantics. It offers a state of the art evaluation of modern and present learn in computational semantics, together with descriptions of recent equipment for developing and bettering assets for semantic computation, similar to WordNet, VerbNet, and semantically annotated corpora. It additionally offers new statistical tools in semantic computation, akin to 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 last word problem in normal language processing, and the major to quite a lot of intriguing 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, laptop technology, Cognitive technology, and synthetic Intelligence.
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Extra resources for Computing Meaning: Volume 4
Garrette et al. word meaning have been used successfully to judge paraphrase appropriateness by representing the meaning of a word in context as a point in a high-dimensional semantics space (Erk and Padó 2008; Thater et al. 2010; Reisinger and Mooney 2010; Dinu and Lapata 2010; Van de Cruys et al. 2011). However, these models only address word meaning, and do not address the question of providing meaning representations for complete sentences. It is a long-standing open question how best to integrate the weighted or probabilistic information coming from such modules with logic-based representations in a way that allows for reasoning over both.
2 Hypernymy According to our definition of ζ above, we construct inference rules of the form ∀x1 , . . , xn [P (x1 , . . , xn ) → Q(x1 , . . xn )] where Q is a synonym or hypernym of P . Thus, for two synonyms A and B, we will generate rules A → B and B → A. However, for hypernym relationships, we only construct the inference rule entailing up the hierarchy: from the hyponym to the hypernym. This is important for licensing correct inferences. Consider example (4). (4) p: Ed owns a car. h: Ed has a vehicle.
Not_ verb (e) ∧ theme(e, p))] → NEG(p)] If the verb is non-entailing in either positive or negative contexts, then we do not generate a rule for that context polarity. This approach works for arbitrarily long chains of nested implicatives and factives. For example, consider the entailment in (8). (8) Dave managed to fail to not forget to leave Dave did not leave Our approach is able to predict this entailment by correctly handling the three nested implicatives along with the negation. Figure 6 shows the nested polarity environments and how the implicative verbs and negations modify the polarity.