从问答讨论中发现语义相关的技术术语和网络资源

贾俊芳; Valeriia TUMANIAN; 李国强

doi:10.1631/FITEE.2000186

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从问答讨论中发现语义相关的技术术语和网络资源

常规文章 | Updated：2021-11-25

- 从问答讨论中发现语义相关的技术术语和网络资源
- Discovering semantically related technical terms and web resources in Q&A discussions
- 信息与电子工程前沿（英文） 2021年22卷第7期页码：969-985
- Affiliations：
  
  School of Computer and Network Engineering, Shanxi Datong University, Datong 037009, China
  School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
- Author bio：
  
  Guoqiang LI, E-mail: li.g@sjtu.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61872232)
- DOI：10.1631/FITEE.2000186
  中图分类号： TP311
- 收稿：2020-04-21，
  
  修回：2021-;6-16，
  
  录用：2020-12-23，
  
  纸质出版：2021-07
- Accepted：
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贾俊芳, Valeriia TUMANIAN, 李国强. 从问答讨论中发现语义相关的技术术语和网络资源[J]. 信息与电子工程前沿（英文）, 2021,22(7):969-985.

Junfang JIA, Valeriia TUMANIAN, Guoqiang LI. Discovering semantically related technical terms and web resources in Q&A discussions[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 969-985.
贾俊芳, Valeriia TUMANIAN, 李国强. 从问答讨论中发现语义相关的技术术语和网络资源[J]. 信息与电子工程前沿（英文）, 2021,22(7):969-985. DOI： 10.1631/FITEE.2000186.

Junfang JIA, Valeriia TUMANIAN, Guoqiang LI. Discovering semantically related technical terms and web resources in Q&A discussions[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 969-985. DOI： 10.1631/FITEE.2000186.

摘要

目前网络上拥有大量可用于软件工程实践的技术和网络资源，并且这个数量还在持续增长。发现语义相似或相关的技术术语和网络资源，可以设计吸引人的服务，以促进信息检索和信息发现的机会。本文从问答（Q

A）讨论的社区中提取技术术语和网络资源，并提出一种基于神经网络语言模型的技术术语和网络资源在联合低维向量空间中的语义表示方法。方法仅基于讨论线程中技术术语（或网络资源）的周围技术术语和web资源，将技术术语和网络资源映射到语义向量空间，而不需挖掘讨论的文本内容。将方法应用于2018年3月的堆栈溢出数据转储。对聚类、搜索和语义推理任务的定量和定性分析表明，所学习的技术术语和网络资源向量表示可以捕获技术术语和网络资源的语义相关性，通过简单的

近邻搜索和在嵌入空间中对学习的向量表示作简单的代数运算，可以支持各种搜索和语义推理任务。

Abstract

A sheer number of techniques and web resources are available for software engineering practice and this number continues to grow. Discovering semantically similar or related technical terms and web resources offers the opportunity to design appealing services to facilitate information retrieval and information discovery. In this study

we extract technical terms and web resources from a community of question and answer (Q

A) discussions and propose an approach based on a neural language model to learn the semantic representations of technical terms and web resources in a joint low-dimensional vector space. Our approach maps technical terms and web resources to a semantic vector space based only on the surrounding technical terms and web resources of a technical term (or web resource) in a discussion thread

without the need for mining the text content of the discussion. We apply our approach to Stack Overflow data dump of March 2018. Through both quantitative and qualitative analyses in the clustering

and semantic reasoning tasks

we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of technical terms and web resources

and they can be exploited to support various search and semantic reasoning tasks

by means of simple

-nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.

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