An Empirical Comparison of Question Classification Methods for Question Answering Systems

Abstract

Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, ``Who is the prime minister of the United Kingdom?'' demands a name of a PERSON, while ``When was the queen of the United Kingdom born?'' entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitable for low-resourced languages.

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