A Stacked Sub-Word Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
A Stacked Sub-Word Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
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发表时间：
2011-06
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通讯作者：
Weiwei SUN
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作者：
Weiwei SUN
联合分词和词性标注的组合搜索空间大，使得高效解码非常困难。因此，表示丰富上下文的有效高阶特征不方便使用。在这项工作中，我们提出了一种新的堆叠子字模型，这一任务，同时考虑到效率和有效性。我们的解决方案是一个两步的过程。首先，一个基于词的分割器，一个基于字符的分割器和一个本地字符分类器进行训练，以产生粗分割和POS信息。其次，三个预测器的输出被合并成子词序列，这些子词序列被进一步括起来，并由细粒度子词标注器用POS标签进行标注。粗到细的搜索方案是有效的，而在子词标记步骤中，可以近似地导出丰富的上下文特征。在宾夕法尼亚大学中国树库上的评价表明，我们的模型比文献中报道的最好的系统有所改进。
The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to produce coarse segmentation and POS information. Second, the outputs of the three predictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tagger. The coarse-to-fine search scheme is efficient, while in the sub-word tagging step rich contextual features can be approximately derived. Evaluation on the Penn Chinese Tree-bank shows that our model yields improvements over the best system reported in the literature.