Adapting taggers to Twitter with not-so-distant supervision

机译：在不太遥远的监督下使标记者适应Twitter

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We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly good source of not-so-distant supervision is linked websites. Specifically, with this source of supervision we are able to improve over the state-of-the-art for Twitter POS tagging (89.76% accuracy, 8% error reduction) and NER (F1=79.4%, 10% error reduction).

机译：我们尝试使用远程监督的不同来源来指导词性（POS）和命名实体标记（NER）的非监督和半监督适应Twitter。我们表明，链接的网站是不太远的监管的一个很好的来源。具体来说，借助这种监管来源，我们可以改善Twitter POS标签（准确度为89.76％，错误减少8％）和NER（F1 = 79.4％，错误减少10％）的最新技术。

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《International conference on computational linguistics》|2014年|1783-1792|共10页
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Barbara Plank; Dirk Hovy; Ryan McDonald; Anders Sogaard;
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Adapting taggers to Twitter with not-so-distant supervision

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