Abstract
This paper deals with automatic classification of questions in the Russian language, a natural early step in building a question answering system. We developed a typology of Russian questions using interrogative particles, pronouns and word order as the main features. A corpus of 2008 questions was manually compiled and annotated according to our typology. We used a fine-grained class set and a coarse-grained one (23 and 14 classes, respectively). The training data, represented as character bi-/trigrams and word uni-/bi-/trigrams, was used to approach the task of question classification. We tested several widely used machine-learning methods (logistic regression, support vector machines, naïve Bayes) against a regular expression baseline on a held-out test corpus annotated by an external expert. The best results were achieved by a SVM classifier (linear kernel) that achieved the accuracy of 65.3% (fine-grained) and 68.7% (coarse-grained), while the baseline regular expression model showed 52.7% accuracy.
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Nikolaev, K., Malafeev, A. (2018). Russian-Language Question Classification: A New Typology and First Results. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_7
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