| learning question classifiers in order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. these constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. this paper presents a machine learning approach to question classification. we learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. we show accurate results on a large collection of free-form questions used in trec 10. we assign one of fifty possible types to a question based on features present in the question. we have developed a machine learning approach which uses the snow learning architecture. |