QA4GIS: A novel approach learning to answer GIS developer questions with API documentation Academic Article uri icon

abstract

  • AbstractCommunitybased question answering websites have attracted more and more scholars and developers to discuss domain knowledge and software development. In this article, we focus on the GIS section of the Stack Exchange website and develop a novel approach, QA4GIS, a deep learningbased system for question answering tasks with a deep neural network (DNN) model to extract the representation of the queryAPI document pair. We use the LambdaMART model to rerank the candidate API documents. We begin with an empirical analysis of the questions and answers, demonstrating that API documents could answer 52.93% of the questions. Then we evaluate QA4GIS by comparing it with 10 other baselines. The experiment results show that QA4GIS can improve 21.39% on the MAP score and 22.34% on the MRR score compared with the best baseline SIF.

published proceedings

  • TRANSACTIONS IN GIS

author list (cited authors)

  • Wang, W., Li, Y. i., Wang, S., & Ye, X.

citation count

  • 2

complete list of authors

  • Wang, Wenbo||Li, Yi||Wang, Shaohua||Ye, Xinyue

publication date

  • October 2021

publisher