The Maximum Entropy model has proved to be useful in a large range of different NLP applications as for example POS-tagging, Named Entity Recognition, or Machine Translation. In my talk I will present a Maximum Entropy approach to Question Answering. To build such model I derived a feature set from the Knowledge Master corpus and computed the model parameters by means of the maxent toolkit by Zhang Le. I will compare my approach with the results of Ravichandran, Hovy, and Och (2003) who built up a baseline QA system with only four features.
 
 
Ravichandran, Hovy, and Och (2003): Statistical QA - Classifier vs. Re-ranker: What's the difference? In: Proceedings of the ACL Workshop on Multilingual Summarization and Question Answering - Machine Learning and Beyond, Sapporo, Japan.