Unsupervised Parsing for Generating Surface-Based Relation Extraction Patterns

Abstract

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 surfacebased 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.