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Robust German Noun Chunking With a Probabilistic Context-Free Grammar

机译:强大的德国名词块与概率无线无线语法

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We present a noun chunker for German which is based on a head-lexicalised probabilistic context-free grammar. A manually developed grammar was semi-automatically extended with robustness rules in order to allow parsing of unrestricted text. The model parameters were learned from unlabelled training data by a probabilistic context-free parser. For extracting noun chunks, the parser generates all possible noun chunk analyses, scores them with a novel algorithm which maximizes the best chunk sequence criterion, and chooses the most probable chunk sequence. An evaluation of the chunker on 2,140 hand-annotated noun chunks yielded 92% recall and 93% precision.
机译:我们为德语提供了一个名词块,其基于头部词汇表达的概率无线语法。手动开发的语法是用稳健性规则进行半自动扩展的,以便解析不受限制的文本。通过概率无线的无线解析器从未标记的训练数据中学习了模型参数。对于提取名词块,解析器生成所有可能的名词块分析,用一种新颖的算法进行分数,该算法最大化最佳的块序列标准,并选择最可能的块序列。对2,140个手动注释的名词块的块的评估产生了92%的召回和93%的精度。

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