Paper: Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing

ACL ID P10-1037
Title Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

We investigate active learning methods for Japanese dependency parsing. We propose active learning methods of using partial dependency relations in a given sentence for parsing and evaluate their effective- ness empirically. Furthermore, we utilize syntactic constraints of Japanese to ob- tain more labeled examples from precious labeled ones that annotators give. Ex- perimental results show that our proposed methods improve considerably the learn- ing curve of Japanese dependency parsing. In order to achieve an accuracy of over 88.3%, one of our methods requires only 34.4% of labeled examples as compared to passive learning.