By Harry Bunt, Johan Bos, Stephen Pulman
This e-book is a suite of papers via top researchers in computational semantics. It provides a cutting-edge review of modern and present learn in computational semantics, together with descriptions of latest equipment for developing and bettering assets for semantic computation, akin to WordNet, VerbNet, and semantically annotated corpora. It additionally provides new statistical tools in semantic computation, reminiscent of 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 normal language processing, and the major to a variety of fascinating functions. The breadth and intensity of insurance of this ebook makes it appropriate as a reference and evaluation of the country of the sector for researchers in Computational Linguistics, Semantics, desktop technological know-how, Cognitive technology, and synthetic Intelligence.
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Additional resources for Computing Meaning: Volume 4
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. , & Reyle, U. (1993). From discourse to logic; An introduction to modeltheoretic semantics of natural language, formal logic and DRT.
In our view, this leads to what are perhaps overly complex semantic representations of some basic linguistic constructions. In contrast, in the method we presented, these two concerns (meaning representation and semantic construction) are separated, enabling us to keep the semantics of constituents simple, while turning the construction of semantic expressions into a separate structured learning problem (with its own internal prediction and decoding mechanisms). Although in the experiments we reported here we do prepare the training data from a traditional treebank, we are encouraged by the results and believe that annotation of a corpus with only semantic expressions is sufficient for building an efficient and reasonably accurate text-to-semantics mapper.
We chose to use Boxer for two main reasons. First, Boxer is a wide-coverage system that can deal with arbitrary text. Second, the DRSs that Boxer produces are close to the standard first-order logical forms that are required for use by the MLN software package Alchemy. Our system interprets discourses with Boxer, augments the resulting logical forms by adding inference rules, and outputs a format that the MLN software Alchemy can read. 5 Ambiguity in Word Meaning In order for our system to be able to make correct natural language inferences, it must be able to handle paraphrasing.