By Guo-Nian Wang, Yi Qin, Min Jiang, Qiu-Rong Zhao (auth.), Donghong Ji, Guozheng Xiao (eds.)
This ebook constitutes conscientiously reviewed and revised chosen papers from the thirteenth chinese language Lexical Semantics Workshop, CLSW 2012, held in Wuhan, China, in July 2012. The sixty seven complete papers and 17 brief papers awarded during this quantity have been conscientiously reviewed and chosen from 169 submissions. they're prepared in topical sections named: purposes on average language processing; corpus linguistics; lexical computation; lexical assets; lexical semantics; new tools for lexical semantics; and different topics.
Read Online or Download Chinese Lexical Semantics: 13th Workshop, CLSW 2012, Wuhan, China, July 6-8, 2012, Revised Selected Papers PDF
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Extra info for Chinese Lexical Semantics: 13th Workshop, CLSW 2012, Wuhan, China, July 6-8, 2012, Revised Selected Papers
Sample text
For two n-grams ng =< w1 wN > and ng ' =< w '1 w 'N > , the similarity of them is defined as follows: sim ngram ( ng , ng ' ) = 1 N N sim ( w , w ' ) i =1 w i (5) i , where sim w ( w1 , w2 ) is similarity between word w1 and w2 , which is defined as: 1 sim w ( w1 , w2 ) = sim wn ( w1 , w2 ) if w1 = w2 else (6) . Equation (6) means that when lexical similarity is calculated, exact matching is first used to determine whether two words are in the same lexical form. If it does, then the similarity is 1; otherwise WordNet is used to calculate their semantic similarity.
This metric has been used in all of the previous compression tasks. In our experiments, we find that 30% words of the original sentence contain the most important information. Bleu which is often used in Machine Translations means the similarity of the predict sentence with the golden target sentence. We find when two system give a similar compression rate, which’s bleu is higher than another also contains more information than another. So, we can use Bleu to evaluate the information the predict sentence contains.
Equation (6) means that when lexical similarity is calculated, exact matching is first used to determine whether two words are in the same lexical form. If it does, then the similarity is 1; otherwise WordNet is used to calculate their semantic similarity. Since WordNet contains only nouns, verbs, adjectives and adverbs, the exact matching can prevent words not in WordNet from being ignored. At the same time, (5) defines n-gram similarity as average rather than minimum or maximum of lexical similarities to ensure fairness to some extent.