LIMSI: Learning Semantic Similarity by Selecting Random Word Subsets
Artem Sokolov · 2012
We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for the semantics modeled. Despite ignoring text preprocessing and dispensing with semantic resources, the approach was ranked as high as 22nd among 89 participants