By Guo-Nian Wang, Yi Qin, Min Jiang, Qiu-Rong Zhao (auth.), Donghong Ji, Guozheng Xiao (eds.)
This booklet 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 offered during this quantity have been conscientiously reviewed and chosen from 169 submissions. they're equipped in topical sections named: functions on common language processing; corpus linguistics; lexical computation; lexical assets; lexical semantics; new equipment for lexical semantics; and different topics.
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Extra info for Chinese Lexical Semantics: 13th Workshop, CLSW 2012, Wuhan, China, July 6-8, 2012, Revised Selected Papers
Example text
Example compressions for the evaluation data Original Sentence Golden OurWork_1 OurWork_2 Original Sentence Golden OurWork_1 OurWork_2 Original Sentence Golden OurWork_1 OurWork_2 6 2009年 2月 20日 清晨 ， 沈 阳 夏宫 成功 爆破 。 沈 阳 夏宫 成功 爆破 沈 阳 夏宫 成功 爆破 沈 阳 夏宫 成功 爆破 据 云南 当地 媒体 消息 ， 目前 “ 躲 猫 猫 ” 事件 调查 委员会 正 在 封闭 完成 调查 报告 ， 报告 内容 即 将 公布 。 躲 猫 猫 事件 调查 委员会 正 封闭 完成 调查 报告 事件 调查 委员会 正 在 封闭 完成 调查 报告 报告 内容 公布 猫 猫 事件 调查 委员会 正 在 封闭 完成 调查 报告 据 英国 广播 公司 1日 报道 ， 流亡 海外 的 泰国 前 总理 他 信 取消 在 香港 的 演讲 。 泰国 前 总理 他 信 取消 在 香港 演讲 泰国 信 取消 在 香港 的 演讲 泰国 前 总理 他 信 取消 在 香港 的 演讲 Conclusion In this paper, Chinese sentence compression is studied for the task of title generation under the framework of deleting words.
Toward a unified framework for standard and update multidocument summarization. cn Abstract. Event argument extraction is an important component of event extraction which plays a decisive role in whether event extraction can be applied to the actual. This paper proposes a method of event argument extraction based on Conditional Random Fields (CRFs). , lexical, semantic, dependency, syntactic and relative-position. More importantly, we propose using semantic role as a specific feature. Great efforts have been made to evaluate the performance by exploring various features and their combination.
This paper uses the method proposed by [8] to calculate lexical semantic similarity in WordNet: sim lin ( c1 , c2 ) = 2 log P ( c 0 ) log P ( c1 ) + log P ( c2 ) (3) , where c1 and c2 are two synsets in WordNet, c0 is the lowest level of word set which subsumes c1 and c2 , and P ( c ) is the probability of an object belonging to the synset c . Since a polysemous word appears in more than one synsets, the similarity of two such words would meet a number of different values. In this paper, we select the maximum of all lexical similarities: sim wn ( w2 , w2 ) = max {sim lin ( c1 , c2 ) | c1 ∈ synsets ( w1 ) , c2 ∈ synsets ( w2 )} .