Superior and efficient fully unsupervised pattern-based concept acquisition using an unsupervised parser
Dmitry Davidov, Roi Reichart, Ari Rappoport · 2009
Sets of lexical items sharing a significant aspect of their meaning (concepts) are fundamental for linguistics and NLP. Unsupervised concept acquisition algorithms have been shown to produce good results, and are preferable over manual preparation of concept resources, which is labor intensive, error prone and somewhat arbitrary. Some existing concept mining methods utilize supervised language-specific modules such as POS taggers and computationally intensive parsers.