Paper: Combining Supervised and Unsupervised Parsing for Distributional Similarity

ACL ID C14-1136
Title Combining Supervised and Unsupervised Parsing for Distributional Similarity
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we address the role of syntactic parsing for distributional similarity. On the one hand, we are exploring distributional similarities as an extrinsic test bed for unsupervised parsers. On the other hand, we explore whether single unsupervised parsers, or their combination, can contribute to better distributional similarities, or even replace supervised parsing as a prepro- cessing step for word similarity. We evaluate distributional thesauri against manually created taxonomies both for English and German for five unsupervised parsers. While for English, a supervised parser is the best single parser in this evaluation, we find an unsupervised parser to work best for German. For both languages, we show significant improvements in word similarity when combining features from su...