Word clustering relies on the creation of a word by word matrix from
bigram or co-occurrence features. The first word in these pairs serves as
the row, the second word serves as the column, and the cell contains the
association score, frequency count, or binary value indicating the
relationship between the pair of words.
This matrix can be reduced via SVD, and is reconstructed such that the original rows are preserved and the columns are reduced. Thus the rows are clustered such that word that occur with similar words in bigrams or co-occurrences are grouped together.
Note that the word matrix used is identical to that which is used in creating the second order representation for context discrimination.
The input must be a Senseval-2 formatted test file. It can be either headed or headless. Even if the data has target words (marked with head tags) the test_scope option and target co-occurrence features are not available. Only bigram or co-occurrence features may be used, and it should be understand that the first word in the bigram or co-occurrence pairs is what will be clustered. A separate set of feature selection data (ie., training data) may not be used with word clustering.