多语言平行语料库中复杂名词短语的标注

Jingxiang Cao, Degen Huang
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引用次数: 0

摘要

名词短语是自然语言语篇中的主导结构。虽然碱基神经网络(BNP)和最大长度神经网络(MNP)相对容易识别和提取,但神经网络的内部结构是自然语言处理中的一个挑战。Penn Treebank使bnp平面为隐式右分支。Vadas和Curran将BNP的内部结构添加到宾夕法尼亚树银行。但BNP结构的结果往往是不正确的,当它被考虑在一个更长的复杂NP (CNP)。结构歧义在大多数CNPs中普遍存在,多语比较有助于消除歧义。本文提出了一种新的NP标注方案,该方案适用于多语言并行语料库,能够区分真正的平分支和右分支。为了实现语言间的一致性,在适当的情况下,首选平面分支而不是二元分支。作为构建CNPs结构和语义分析的黄金标准语料库的试点任务,从联合国决议中提取了381个文件标题作为CNPs的典型例子。中文、英文和俄文的文档标题被手工标注为XML格式,希望能够帮助针对cnp的解析器或机器翻译获得规则。报告遇到的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Annotation of complex noun phrases from multilingual parallel corpus
The Noun Phrase (NP) is the dominant construct in natural language text. While base NPs (BNP) and maximal length NPs (MNP) are relatively easy to identified and extracted, the internal structure of NPs is rather a challenge in natural language processing. Penn Treebank leaves the BNPs flat as implicit right branching. Vadas and Curran added BNP internal structure to the Penn Treebank. But the results of the BNP structure are very often incorrect when it is considered within a longer complex NP (CNP). Structural ambiguity prevails in most CNPs and multilingual comparison may help improve disambiguation. We introduce a new NP annotation scheme, which is applicable to multilingual parallel corpora and discriminate genuine flat branching and right branching. Flat branching is preferred instead of binary branching wherever appropriate so as to achieve inter-lingual consistency. As a pilot task to build a gold standard corpus for structural and semantic analysis of CNPs, 381 document titles are extracted from the UN resolutions as typical examples of CNPs. Document titles in Chinese, English and Russian are manually annotated in XML format with the hope to help acquire rules for parsers or machine translators targeted at CNPs. The problems encountered are reported.
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