首页> 外文会议>ACL-05; Association for Computational Linguistics Annual Meeting; 20050625-30; Ann Arbor,MI(US) >Lexicalization in Crosslinguistic Probabilistic Parsing: The Case of French
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Lexicalization in Crosslinguistic Probabilistic Parsing: The Case of French

机译:跨语言概率分析中的词汇化:以法语为例

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This paper presents the first probabilistic parsing results for French, using the recently released French Treebank. We start with an unlexicalized PCFG as a baseline model, which is enriched to the level of Collins' Model 2 by adding lexical-ization and subcategorization. The lexi-calized sister-head model and a bigram model are also tested, to deal with the flatness of the French Treebank. The bigram model achieves the best performance: 81% constituency F-score and 84% dependency accuracy. All lexicalized models outperform the unlexicalized baseline, consistent with probabilistic parsing results for English, but contrary to results for German, where lexicalization has only a limited effect on parsing performance.
机译:本文使用最近发布的法语树库,介绍了法语的第一个概率解析结果。我们从未词汇化的PCFG作为基线模型开始,通过添加词汇化和子类别将其丰富到Collins的Model 2。还测试了lexi校准的姐妹头模型和bigram模型,以处理法国树银行的平坦性。 bigram模型实现了最佳性能:81%的选区F得分和84%的依赖度准确性。所有词汇化模型均优于非词汇化基线,这与英语的概率解析结果一致,但与德语的结果相反,德语的词汇化模型对解析性能的影响有限。

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