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High-precision Identification of Discourse New and Unique Noun Phrases

机译:话语新奇名词短语的高精度识别

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摘要

Coreference resolution systems usually attempt to find a suitable antecedent for (almost) every noun phrase. Recent studies, however, show that many definite NPs are not anaphoric. The same claim, obviously, holds for the indefinites as well. In this study we try to learn automatically two classifications, +-discourse_new and +-unique, relevant for this problem. We use a small training corpus (MUC-7), but also acquire some data from the Internet. Combining our classifiers sequentially, we achieve 88.9% precision and 84.6% recall for discourse new entities. We expect our classifiers to provide a good prefiltering for coreference resolution systems, improving both their speed and performance.
机译:共指解析系统通常尝试为(几乎)每个名词短语找到合适的先行词。但是,最近的研究表明,许多确定的NP并不是照应的。显然,相同的主张也适用于不定式。在这项研究中,我们尝试自动学习与该问题相关的两个分类,+-discourse_new和+ -unique。我们使用小型训练语料库(MUC-7),但也从Internet获取一些数据。依次结合我们的分类器,我们对语篇新实体的准确率达到88.9%,召回率达到84.6%。我们希望我们的分类器能够为共指解析系统提供良好的预过滤,从而提高其速度和性能。

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