Discovering semantically related technical terms and web resources in Q&A discussions  

作　　者：Junfang JIA Valeriia TUMANIAN Guoqiang LI 

机构地区：[1]School of Computer and Network Engineering,Shanxi Datong University,Datong 037009,China [2]School of Software,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2021年第7期969-985,共17页信息与电子工程前沿（英文版）

基　　金：the National Natural Science Foundation of China(No.61872232)。

摘　　要：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,search,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 K-nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.

关 键 词：Technical terms Web resources Word embedding Q&A web site Clustering tasks Recommendation tasks 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]