Using LSA and noun coordination information to improve the precision and recall of automatic hyponymy extraction

In this paper we demonstrate methods of im-proving both the recall and the precision of au-tomatic methods for extraction of hyponymy (IS A) relations from free text. By applying la-tent semantic analysis (LSA) to filter extracted hyponymy relations we reduce the rate of er-ror of our initial pattern-based hyponymy ex-traction by 30%, achieving precision of 58%. Applying a graph-based model of noun-noun similarity learned automatically from coordi-nation patterns to previously extracted correct hyponymy relations, we achieve roughly a five-fold increase in the number of correct hy-ponymy relations extracted.