Semantics with Dense Vectors > We will introduce three methods of generating very dense, short vectors:
> 1. using dimensionality reduction methods like SVD,
> 2. using neural nets like the popular skip-gram or CBOW approaches.
> 3. a quite different approach based on neighboring words called Brown clustering.
Word Meaning and Similarity - Stanford University thesaurus based meaning, Distributional models of meaning
Term-Context matrix. Term-document matrix: use tf-idf instead of raw term counts, for the term-context matrix, use Positive Pointwise Mutual Information (PPMI: Do words x and y co-occur more than if they were independent?)