Unsupervised Parsing for Generating Surface-Based Relation Extraction Patterns
J. Illig, B. Roth, and D. Klakow. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers, page 100--105. Gothenburg, Sweden, Association for Computational Linguistics, (April 2014)
Finding the right features and patterns for identifying relations in natural language is one of the most pressing research questions for relation extraction. In this paper, we compare patterns based on supervised and unsupervised syntactic parsing and present a simple method for extracting surface patterns from a parsed training set. Results show that the use of surface-based patterns not only increases extraction speed, but also improves the quality of the extracted relations. We find that, in this setting, unsupervised parsing, besides requiring less resources, compares favorably in terms of extraction quality.