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

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. 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 symmetric patterns and graph-based concept discovery algorithm. We evaluate our algorithm on very large corpora in English and Russian, using both human judgments and WordNet-based evaluation. Using similar settings as the leading fully unsupervised previous work, we show a significant improvement in concept quality and in the extraction of multiword expressions. Our method is the first to use fully unsupervised parsing for unsupervised concept discovery, and requires no language-specific tools or pattern/word seeds.