Our implementation of Latent Semantic Analysis supports clustering of
similar contexts and clustering of lexical features. This provides the
same functionality as is available in the native SenseClusters
methodology, but using a different underlying representation.
Traditionally LSA represents text using a term by document matrix. Our implementation generalizes this to a feature by context matrix, where terms are but one kind of feature, and documents on kind of context. Features may be unigrams, bigrams, co-occurrences, and target co-occurrences. Contexts may be units of text of any length, although typically they are sentences, paragraphs, or short articles.
The basic assumption behind LSA feature clustering is that features can be differentiated from each other and divided into classes or clusters based on the contexts in which they occur. Features that occur in similar contexts are assumed to be similar to each other. A similar assumption underlies LSA context clustering, in that contexts that are made up of features that have occurred in similar contexts should be considered similar to each other.