Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing
Manabu Sassano, Sadao Kurohashi(Kyoto University)
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), 2010/7 pp.356-365
自然言語処理 (Natural Language Processing) 機械学習 (Machine Learning)
- 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 effectiveness empirically. Furthermore, we utilize syntactic constraints of Japanese to obtain more labeled examples from precious labeled ones that annotators give. Experimental results show that our proposed methods improve considerably the learning 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.