Research on question classification based on BI-LSTM

Abstract

Question classification plays an important role in question answering (QA) system, and its results directly affect the quality of QA. Traditional methods of question classification include rule-based methods and statistical machine learning methods. They need to manually summarize rules or extract the features of questions. The rule definition and feature selection are subjective and one-sided, which are not conducive to fully understand the semantic information of questions. Based on the above problem, this paper proposes a question classification model based on Bi-LSTM. This model combines words, part of speech (POS) and position information of words to generate embedded representation of words, and uses Bi-LSTM to classify questions. The method can efficiently extract the local features of questions and simplify feature engineering. The accuracy of coarse-grained classification on the question classification data set of Harbin Institute of Technology (HIT) has reached 92.38%.