By Harry Bunt, Johan Bos, Stephen Pulman
This booklet is a set of papers via best researchers in computational semantics. It offers a cutting-edge assessment of modern and present learn in computational semantics, together with descriptions of recent equipment for developing and enhancing assets for semantic computation, corresponding to WordNet, VerbNet, and semantically annotated corpora. It additionally offers new statistical equipment in semantic computation, similar 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 common language processing, and the foremost to quite a lot of fascinating functions. The breadth and intensity of insurance of this ebook makes it appropriate as a reference and assessment of the kingdom of the sphere for researchers in Computational Linguistics, Semantics, desktop technology, Cognitive technology, and synthetic Intelligence.
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Additional info for Computing Meaning: Volume 4
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.
For example we may only want to consider predicates Q that stand for paraphrases of P . For this reason, the function ζ can be used to limit the predicates Q considered for the right-hand sides of rules. If n ) = P n , then a rule will be generated for every Q ∈ P n . 2 Addressing Polysemy When a word is polysemous, this affects the applicability of vector space-based inference rules. Consider the rule ∀e[fix(e) → correct(e)] (any fixing event is a correcting event): this rule applies in contexts like “fix a problem”, but not in contexts like “fix the date”.
Then n in formula G ∈ L is the contextualized inference projection for predicate P ∈ PL G Πsim,ζ, (P ) = Πsim,ζ, [P /α( (P ),κ(P ,G)) (P ) In this contextualized inference projection, any rule ∀x1 , . . , xn [P (x1 , . . , xn ) → Q(x1 , . . , xn )] is weighted by similarity sim(α( (P ), κ(P , G)), (Q)) between the context-specific vector for P and the vector for Q. This follows common practice in vector space models of word meaning in context of computing a context-specific representation of the target, but not the paraphrase candidate.