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Knowledge-Poor Approach to Shallow Parsing: Contribution of Unsupervised Part-of-Speech Induction

机译：浅谈浅层解析的知识差：无监督术语归档的贡献

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Natural language processing tasks often rely on part-of-speech (POS) tagging as a preprocessing step. However it is not clear how the absence of any part-of-speech tagger should hamper the development of other natural language processing tools. In this paper we investigate the contribution of fully unsupervised part-of-speech induction to a common natural language processing task. We focus on the supervised English shallow parsing task and compare systems relying either on POS induction, on POS tagging, or on lexical features only as a baseline. Our experiments on the English CoNLL'2000 dataset show a significant benefit from POS induction over the baseline, with performances close to those obtained with a traditional POS tagger. Results demonstrate a great potential of POS induction for shallow parsing which could be applied to resource-scarce languages.
机译：自然语言处理任务通常依赖于语音（POS）标记作为预处理步骤。然而，目前尚不清楚缺乏任何言语标签如何妨碍其他自然语言处理工具的发展。在本文中，我们调查了完全无监督的语音归档对共同的自然语言处理任务的贡献。我们专注于监督英语浅层解析任务，并比较在POS归纳上的POS标记或仅作为基线的词汇表的依据。我们对英语Conll'2000数据集的实验显示了基线POS诱导的显着益处，与传统POS标签获得的表演接近。结果表明，POS诱导对于浅层解析的巨大潜力，可以应用于资源稀缺语言。

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