This booklet is a suite of papers via prime researchers in computational semantics. It offers a cutting-edge assessment of modern and present learn in computational semantics, together with descriptions of latest equipment for developing and enhancing assets for semantic computation, corresponding to WordNet, VerbNet, and semantically annotated corpora. It additionally provides new statistical equipment in semantic computation, comparable 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 final word problem in usual language processing, and the major to a variety of intriguing functions. The breadth and intensity of insurance of this booklet makes it appropriate as a reference and assessment of the nation of the sphere for researchers in Computational Linguistics, Semantics, computing device technology, Cognitive technology, and synthetic Intelligence.
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Additional resources for Computing Meaning, Volume 4 (Text, Speech and Language Technology, Volume 47)
Here, hypothesis h1 is a valid entailment, and should be judged to have high probability by the system. Hypothesis h2 should have lower probability since it uses the wrong sense of leave and h3 should be low probability because the logical operator not has reversed the meaning of the premise statement. While the most prominent forum using textual entailment is the Recognizing Textual Entailment (RTE) challenge (Dagan et al. 2005), the RTE datasets do not test the phenomena in which we are interested.
Their approach identifies an “implication signature” for every implicative or factive verb. This signature specifies the truth conditions for the verb’s nested proposition, depending on whether the verb occurs in a positive or negative environment. Following MacCartney and Manning (2009), we write implication signatures as “x/y” where x represents the entailment to which the speaker commits in a positive environment and y represents entailment in a negative environment. 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.
145–158). Stanford: CSLI. , & Crouch, R. (1992). Monotonic semantic interpretation. In Proceedings of the 30th annual meeting of the association for computational linguistics, Newark, Delaware (pp. 32–39). , Curran, J. , & Hockenmaier, J. (2004). Wide-coverage semantic representations from a ccg parser. In Proceedings of the 20th international conference on computational linguistics, Geneva, Switzerland (pp. 1240–1246). , & Mercer, R. (1992). Class-based n-gram models of natural language. Computational Linguistics, 18(4), 467–479.