Paper: Optimizing To Arbitrary NLP Metrics Using Ensemble Selection

ACL ID H05-1068
Title Optimizing To Arbitrary NLP Metrics Using Ensemble Selection
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

While there have been many successful applica- tions of machine learning methods to tasks in NLP, learning algorithms are not typically designed to optimize NLP performance metrics. This paper evaluates an ensemble selection framework de- signed to optimize arbitrary metrics and automate the process of algorithm selection and parameter tuning. We report the results of experiments that in- stantiate the framework for three NLP tasks, using six learning algorithms, a wide variety of parame- terizations, and 15 performance metrics. Based on our results, we make recommendations for subse- quent machine-learning-based research for natural language learning.