Download Computing Meaning, Volume 4 (Text, Speech and Language PDF

This e-book is a set of papers by way of top researchers in computational semantics. It offers a cutting-edge review of modern and present examine in computational semantics, together with descriptions of latest equipment for developing and bettering assets for semantic computation, corresponding to WordNet, VerbNet, and semantically annotated corpora. It additionally offers new statistical tools in semantic computation, equivalent to the appliance of distributional semantics within the compositional calculation of sentence meanings.

Computing the which means of sentences, texts, and spoken or texted discussion is the final word problem in typical language processing, and the major to a variety of fascinating functions. The breadth and intensity of assurance of this publication makes it compatible 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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Additional resources for Computing Meaning, Volume 4 (Text, Speech and Language Technology, Volume 47)

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Note that since weights are not possible when running an example in the theorem prover, any rule that would receive a non-zero weight in an MLN is simply treated as a “hard clause” following Bos and Markert (2005). For the experiments, we generated a vector space from the entire New York Times portion of the English Gigaword corpus (Graff and Cieri 2003). The example entailments evaluated were designed to test the interaction between the logical and weighted phenomena. For example, in (12), “fail to” is a negatively entailing implicative in a positive environment, so according to the theorem prover, p entails both h1 and h2.

Both x and y have three possible values: “+” for positive entailment, meaning the nested proposition is entailed, “-” for negative entailment, meaning the negation of the proposition is entailed, and “o” for “null” entailment, meaning that neither the proposition nor its negation is entailed. 5 3 Example (6) is derived from examples by MacCartney and Manning (2009). 4 Example (7) is adapted from document wsj_0126 from the Penn Treebank. 5 Note that forget to and forget that have different implication signatures.

While it is possible to learn rule weights in an MLN directly from training data, our approach at this time focuses on incorporating weights computed by external knowledge sources. Weights for word meaning rules are computed from the distributional model of lexical meaning and then injected into the MLN. Rules governing implicativity are given infinite weight (hard constraints). We use the open source software package Alchemy (Kok et al. 2005) to perform MLN inference. 3 Linking Logical Form and Vector Spaces In this section we define a link between logical form and vector space representations through a mapping function that connects predicates in logical form to points A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics 31 in vector space.

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