Paper: Logistic Online Learning Methods and Their Application to Incremental Dependency Parsing

ACL ID P07-3009
Title Logistic Online Learning Methods and Their Application to Incremental Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured clas- sification problems. The update rules are based on multinomial logistic models. The most interesting question for such an ap- proach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the applicability of the al- gorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. These tasks were con- ventional multiclass classification, hiearchi- cal classification, and a structured classifica- tion task: complete labeled dependency tree prediction. The performance figures of the logistic algorithms range from slightly lower to slightly highe...