Titlebook: Computational Linguistics and Intelligent Text Processing; 12th International C Alexander F. Gelbukh Conference proceedings 2011 Springer B

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Computational Linguistics and Intelligent Text Processing12th International C

A保存的

jungle

ALE

Measuring Similarity of Word Meaning in Context with Lexical Substitutes and Translationsoverlap with annotations of usage similarity on the same data and show that the overlaps in paraphrases or translations also correlate with the similarity judgements. This bodes well for using any of these methods to evaluate unsupervised representations of lexical semantics. We do however find that

GOAT

fructose

威胁你

https://doi.org/10.1007/978-3-658-42276-9aper, taking grammar in CL and parsing in NLP as an example, we will discuss how to re-integrate these two research disciplines. Research results of our group on parsing are presented to show how grammar in CL is used as the backbone of a parser.

Brittle

https://doi.org/10.1007/978-3-658-43169-3so on) to build dependency structure for Tamil sentences. Our initial results show that, both rule-based and corpus-based approaches achieved the accuracy of more than 74% for the unlabeled task and more than 65% for the labeled tasks. Rule-based parsing accuracy dropped considerably when the input was tagged automatically.

locus-ceruleus

Der wissenschaftliche Publikationsprozessmance of our parser, which needs no syntactic tagged resources or rules, trained with a small corpus, is 10% below to that of commercial semi-supervised dependency analyzers for Spanish, and comparable to the state of the art for English.

对待

Der wissenschaftliche Publikationsprozessrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.