Abstract
The feature selection method we are presenting in this chapter relies on web scale N-gram counts. It uses counts collected from the web in order to rank candidates. Features are thus created from unlabeled data, a strategy which is part of a growing trend in natural language processing. Disambiguation results obtained by web N-gram feature selection will be compared to those of previous approaches that equally rely on an underlying Naïve Bayes model but on completely different feature sets. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will show that web N-gram feature selection for the Naïve Bayes model is a reliable alternative to other existing approaches, provided that a “quality list” of features, adapted to the part of speech, is used.
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Notes
- 1.
Which are the same as those considered in Chap. 3.
- 2.
- 3.
Which are the same as those considered in Chap. 3.
- 4.
Reprinted here from (Preoţiuc and Hristea 2012).
- 5.
Reprinted here from (Preoţiuc and Hristea 2012).
- 6.
Reprinted here from (Preoţiuc and Hristea 2012).
- 7.
Reprinted here from (Preoţiuc and Hristea 2012).
- 8.
Together with Daniel Preoţiuc.
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Hristea, F.T. (2013). N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model . In: The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33693-5_5
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