加泰罗尼亚语的低成本命名实体分类:利用多语言资源和未标记数据

NER@ACL Pub Date : 2003-07-12 DOI:10.3115/1119384.1119388
Lluís Màrquez i Villodre, A. Gispert, X. Carreras, Lluís Padró
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引用次数: 6

摘要

这项工作研究命名实体分类(NEC)加泰罗尼亚语没有利用该语言的大量注释资源。我们探索和比较了两种观点,即单独利用加泰罗尼亚语资源,以及直接训练双语分类模型(西班牙语和加泰罗尼亚语),因为西班牙语有大量带注释的示例。在真实数据上获得的实证结果表明,多语言模型明显优于单语言模型,并且通过对未标记数据的自举更容易改进加泰罗尼亚语NEC模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-cost Named Entity Classification for Catalan: Exploiting Multilingual Resources and Unlabeled Data
This work studies Named Entity Classification (NEC) for Catalan without making use of large annotated resources of this language. Two views are explored and compared, namely exploiting solely the Catalan resources, and a direct training of bilingual classification models (Spanish and Catalan), given that a large collection of annotated examples is available for Spanish. The empirical results obtained on real data point out that multilingual models clearly outperform monolingual ones, and that the resulting Catalan NEC models are easier to improve by bootstrapping on unlabelled data.
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