Titlebook: Semi-Supervised Learning and Domain Adaptation in Natural Language Processing; Anders Søgaard Book 2013 Springer Nature Switzerland AG 201

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1947-4040 hese algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. Th

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Supervised and Unsupervised Prediction,n NLP. The main focus will be classification. Classification algorithms simply estimate functions . : . ↦ . from multi-dimensional input space . to a set of discrete classes .. Consider, for example, the problem of estimating the polarity of movie reviews—or better, consider ..

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Semi-Supervised Learning,s than labeled data, to learn better models than with labeled data alone. I tell my son that . is a cow and . is a horse, but then he starts to label other four-legged animals he sees in the countryside, gradually refining his decision boundary.. However, semi-supervised learning also often leads to

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Learning under Bias,. In the literature, it is common to talk about different kinds of data bias. Data may be slightly differently distributed because of a .. Say we want to build a language model, but, for various reasons, we only have a corpus of sentences of less than 40 words. Our sample is biased, overrepresenting

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Anders Søgaardnlichen Wechselstromdampfmaschine vergleicht, findet man, daß beide Auspuffzeiten ungefähr im Verhältnis von 1 : 2 stehen. Der in der Gleichstromdampfmaschine arbeitende Dampf muß also in der Hälfte der Zeit nach dem Kondensator befördert werden. Nun besteht aber die Tatsache, daß bei der jetzt übli

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Anders Søgaardnlichen Wechselstromdampfmaschine vergleicht, findet man, daß beide Auspuffzeiten ungefähr im Verhältnis von 1 : 2 stehen. Der in der Gleichstromdampfmaschine arbeitende Dampf muß also in der Hälfte der Zeit nach dem Kondensator befördert werden. Nun besteht aber die Tatsache, daß bei der jetzt übli

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