SVD and Clustering for Unsupervised POS Tagging
SVD and Clustering for Unsupervised POS Tagging
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用于无监督 POS 标记的 SVD 和聚类
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
E. Bienenstock
中科院分区：
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作者：
M. Lamar;Y. Maron;Mark Johnson;E. Bienenstock
我们回顾了Schutze(1995)的无监督词性标注算法。该算法使用降维奇异值分解，然后进行聚类，从上下文分布中提取潜在特征。正如在这里实施的那样，它以比最近的方法低得多的成本实现了最先进的标记准确性。它还可以产生一系列更细粒度的标记，潜在地应用于各种任务。
We revisit the algorithm of Schutze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.