Question Answering System for Incomplete and Noisy Data

We present a question answering system that can handle noisy and incomplete natural language data, and methods and measures for the evaluation of question answering systems. Our question answering system is based on the vector space model and linguistic analysis of the natural language data. In the evaluation procedure, we test eight different preprocessing schemes for the data, and come to the conclusion that lemmatization combined with breaking compound words into their constituents gives significantly better results than the baseline. The evaluation process is based on stratified random sampling and bootstrapping. To measure the correctness of an answer, we use partial credits as well as full credits.