Learning to Classify Questions
نویسنده
چکیده
An automatic classifier of questions in terms of their expected answer type is a desirable component of many question-answering systems. It eliminates the manual labour and the lack of portability of classifying them semi-automatically. We explore the performance of several learning algorithms (SVM, neural networks, boosting) based on two purely lexical feature sets on a dataset of almost 2000 TREC questions. We compare the performance of these methods on the questions in English and their translation in French.
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تاریخ انتشار 2005