Paper: Superior and Efficient Fully Unsupervised Pattern-based Concept Acquisition Using an Unsupervised Parser

ACL ID W09-1108
Title Superior and Efficient Fully Unsupervised Pattern-based Concept Acquisition Using an Unsupervised Parser
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009
Authors

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fun- damental for linguistics and NLP. Unsuper- vised concept acquisition algorithms have been shown to produce good results, and are preferable over manual preparation of con- cept resources, which is labor intensive, er- ror prone and somewhat arbitrary. Some ex- isting concept mining methods utilize super- vised language-specific modules such as POS taggers and computationally intensive parsers. In this paper we present an efficient fully unsupervised concept acquisition algorithm that uses syntactic information obtained from a fully unsupervised parser. Our algorithm incorporates the bracketings induced by the parser into the meta-patterns used by a sym- metric patterns and graph-based concept dis- cover...