Combining Supervised and Unsupervised Parsing for Distributional Similarity
Martin Johannes Riedl, Irina Alles, Chris Biemann · International Conference on Computational Linguistics · 2014
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 preprocessing 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 supervised and unsupervised parsers. To our knowledge, this is the first work where unsupervised parsers are systematically evaluated extrinsically in a semantic task, and the first work to show that unsupervised parsing can complement and even replace supervised parsing, when used as a pre-processing feature.