A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09
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.doi:10.3115/1596374.1596386 fatcat:gcwy5xuq4zb5rikml73aixbk2a