Paper: Predicate Argument Structure Analysis Using Transformation Based Learning

ACL ID P10-2030
Title Predicate Argument Structure Analysis Using Transformation Based Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Maintaining high annotation consistency in large corpora is crucial for statistical learning; however, such work is hard, especially for tasks containing semantic elements. This paper describes predi- cate argument structure analysis usingy transformation-based learning. An advan- tage of transformation-based learning is the readability of learned rules. A dis- advantage is that the rule extraction pro- cedure is time-consuming. We present incremental-based, transformation-based learning for semantic processing tasks. As an example, we deal with Japanese pred- icate argument analysis and show some tendencies of annotators for constructing a corpus with our method.