Paper Info
Title
Feature Analysis for Duplicate Detection in Programming QA Communities.
Abstract
In community question answering (CQA), duplicate questions are questions that were previously created and answered but occur again. These questions produce noises in the CQA websites which impede users to find answers efficiently. Programming CQA (PCQA), a branch of CQA that holds questions related to programming, also suffers from this problem. Existing works on duplicate detection in PCQA websites framed the task as a supervised learning task on the question pairs, and relied on a number of extracted features of the question pairs. But they extracted only textual features and did not consider the source code in the questions, which are linguistically very different to natural languages. Our work focuses on developing novel features for PCQA duplicate detection. We leverage continuous word vectors from the deep learning literature, probabilistic models in information retrieval and association pairs mined from duplicate questions using machine translation. We provide extensive empirical analysis on the performance of these features and their various combinations using a range of learning models. Our work could be helpful for both research works and practical applications that require extracting features from texts that are not all natural languages.
Year
DOI
Venue
2017
10.1007/978-3-319-69179-4_44
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017
Keywords
Field
DocType
Feature analysis,Question answering,Duplicate detection
Data mining,Question answering,Information retrieval,Source code,Computer science,Machine translation,Supervised learning,Natural language,Artificial intelligence,Deep learning,Probabilistic logic,Pattern recognition (psychology)
Conference
Volume
ISSN
Citations 
10604
0302-9743
3
PageRank 
References 
Authors
0.36
21
4
Name
Order
Citations
PageRank
Wei Emma Zhang16921.52
Quan Z. Sheng23520301.77
Yanjun Shu393.83
Vanh Khuyen Nguyen430.36