Query Answerability Classifier for Direct Answer Module in Web Search Engines

Bachelor thesis (2021)

Authors

Y. Wang Electrical Engineering, Mathematics and Computer Science

Contributors

C. Hauff Web Information Systems - EEMCS (mentor)
G. Iosifidis Embedded Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Yiran Wang
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Published Date

30-06-2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In order determine when we can show direct answer module to user queries in web search engine, an independent classifier is designed in this study to assess the answerability of each user query. Real user queries are sampled from MS MARCO Question Answering and Natural Langauge Generation dataset \cite{MSMARCO} and manually labelled with query answerability to train and evaluate the classifier. As a result, the XGboost model has an overall better performance than the random forest model with prediction accuracy score 0.83 and F1 score 0.89. Once the classifier determines the user query is answerable, a MRC model may be used to find the direct answer within provided passages. Else, no direct answer shall be provided to this query.