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<title>Feature Clustering (LSA)</title>
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<h1>Feature Clustering (LSA)</h1>
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Lexical features (unigrams, bigrams, co-occurrences, and target
co-occurrences) are clustered in the LSA methodology based on the
contexts in which they occur.
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This relies on a feature by context representation of the data, which
indicates the contexts in which a given feature occurs. This is to be
contrasted with the word (unigram) clustering supported by the native
SenseClusters methodology, which clusters words based on the words with
which they co-occur.
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The input must be a Senseval-2 formatted test file. It can be either
headed or headless. Even if the data has target words (headed) the
test_scope option and target co-occurrence features are not available.
A separate set of feature selection data (ie., training data) may
not be used with feature clustering.