Computing Meaning: Volume 4 by Harry Bunt, Johan Bos, Stephen Pulman

By Harry Bunt, Johan Bos, Stephen Pulman

This publication is a suite of papers via prime researchers in computational semantics. It offers a cutting-edge evaluate of modern and present study in computational semantics, together with descriptions of recent equipment for developing and enhancing assets for semantic computation, reminiscent of 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 that means of sentences, texts, and spoken or texted discussion is the final word problem in ordinary language processing, and the foremost to quite a lot of interesting functions. The breadth and intensity of assurance of this publication makes it appropriate as a reference and assessment of the kingdom of the sphere for researchers in Computational Linguistics, Semantics, machine technology, Cognitive technological know-how, and synthetic Intelligence. ​

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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. 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.

We also write V for the set of points in the space. 1 The central relation in vector spaces is semantic similarity. We represent this through a similarity function sim : V × V → [0, 1] that maps each pair of points in vector space to their degree of similarity. While most similarity functions in the literature are symmetric, such that sim(v, w) = sim(w, v), our definition also accommodates asymmetric similarity measures like Kotlerman et al. (2010). We link logical form and a vector space through a function that maps every predicate symbol to a point in space.

We then limit the training set to instances that only include the most frequent k labels, for k = 100, 50, 25, 12, while keeping the test set the same. As can be seen in Table 7, the accuracy is unaffected when the training set is limited to the 100 most frequent or 50 most frequent labels. There is a slight loss when training is limited to 25 labels and a large loss if it is limited to 12 labels. This appears to show that, for this corpus, the core label set needed to construct the majority of semantic expressions has a size somewhere between 25 and 50.

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