Community-based 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 learning-based system for question answering tasks with a deep neural network (DNN) model to extract the representation of the query–API 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.

中文翻译：

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QA4GIS：一种学习用 API 文档回答 GIS 开发人员问题的新方法

基于社区的问答网站吸引了越来越多的学者和开发者讨论领域知识和软件开发。在本文中，我们重点关注 Stack Exchange 网站的 GIS 部分，并开发了一种新颖的方法 QA4GIS，这是一种基于深度学习的问答任务系统，使用深​​度神经网络 (DNN) 模型来提取查询的表示。 “API 文档对。我们使用 LambdaMART 模型重新排列候选 API 文档。我们首先对问题和答案进行实证分析，证明 API 文档可以回答 52.93% 的问题。然后我们通过与其他 10 个基线进行比较来评估 QA4GIS。实验结果表明，与最佳基线SIF相比，QA4GIS在MAP得分上提高了21.39%，在MRR得分上提高了22.34%。