This ebook is a suite of papers by way of top researchers in computational semantics. It provides a state of the art assessment of contemporary and present learn 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 offers new statistical tools in semantic computation, resembling 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 final word problem in traditional language processing, and the most important to quite a lot of intriguing functions. The breadth and intensity of assurance of this booklet makes it appropriate as a reference and review of the kingdom of the sector for researchers in Computational Linguistics, Semantics, desktop 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)
1012–1022). Annotations that Effectively Contribute to Semantic Interpretation Harry Bunt Abstract This chapter presents a new perspective on the use of semantic annotations. It is argued that semantic annotations should themselves have a semantics in order to be really useful. It is shown that, when this is the case, the information in a semantic annotation can be effectively combined with the results of compositional semantic analysis, with the effect of removing some of the underspecification in a compositional interpretation, or narrowing the interpretation down to one that is appropriate in a given context.
S. S. patents. S. patents. The first-order approach, which contains inference rules for both paraphrases as hard clauses, cannot distinguish between good and bad paraphrases, and considers both of them equally valid. In contrast, the weighted approach can judge the degree of fit of the two potential paraphrases. Also, it does so in a context-specific manner, choosing the paraphrase observe over reward in the context of patents. Our ability to perform a full-scale evaluation is currently limited by problems in the Alchemy software required to perform probabilistic inference.
2004). Conceptual spaces. Cambridge: MIT Press. Getoor, L. & Taskar, B. ) (2007). Introduction to statistical relational learning. Cambridge: MIT Press. , & Domingos, P. (2011). Probabilistic theorem proving. In 27th conference on uncertainty in artificial intelligence (UAI). , & Cieri, C. (2003). English Gigaword. Philadelphia: Linguistic Data Consortium. Harris, Z. (1954). Distributional structure. Word, 10, 146–162. Hobbs, J. , & Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63(1–2), 69–142.