Classification of selected words or phrases in sentential or broader contexts: As noted earlier, examples include WSD, named entity recognition, and sentence boundary detection. The only point of distinction from text/document classification is that it is not a chunk of text as a whole, but rather a word or phrase in the context of such a chunk that is to be classified. Therefore features are chosen to reflect both the features of the target word or phrase (such as morphology) and the way it relates to its context, in terms of, e.g., surrounding words or word categories, (likely) local syntactic dependency relations, and features with broader scope such as word frequencies or document class. Apart from this difference in how features are chosen, the same (supervised) learning and classification methods discussed above can be applied.  However, sufficiently large training corpora may be hard to construct. For example, in statistical WSD (e.g., Yarowsky 1992; Chen et al. 2009),  since thousands of words have multiple senses in sources such as WordNet, it is difficult to  construct  a sense-annotated training corpus that contains sufficiently many occurrences of all of these senses to permit statistical learning. Thus annotations are typically restricted to  the senses of a few polysemous words, and statistical WSD has been shown to be feasible for the selected words, but broad-coverage WSD tools remain elusive.