Vote calibration in community question-answering systems

2012 ◽  
Author(s):  
Bee-Chung Chen ◽  
Anirban Dasgupta ◽  
Xuanhui Wang ◽  
Jie Yang
Keyword(s):  
Question Answering ◽  
Community Question Answering ◽  
Question Answering Systems

scholarly journals Are You Talking about Software Product Lines? An Analysis of Developer Communities

2019 ◽  
Author(s):  
Jacob Krüger
Keyword(s):  
Question Answering ◽  
Practical Knowledge ◽  
Software Product Lines ◽  
Product Line ◽  
Product Lines ◽  
Systematic Analysis ◽  
Technical Problems ◽  
Community Question Answering ◽  
Software Product ◽  
Question Answering Systems

Community-question-answering systems, such as Stack Overflow, provide a platform for various communities to ask questions, dis- cuss topics, and find knowledge. Especially software developers are heavily relying on such systems to identify solutions for their problems. While the content of community-question-answering systems may be less scientific, it usually represents practical knowledge from various perspectives and backgrounds. Thus, analyzing this content can be valuable for the scientific community to understand previous and current (i.e., open questions) needs of practitioners. In this paper, we report a systematic analysis of two websites that comprise communities with a focus on software development: Stack Exchange and Quora. We extract questions, answers, comments, and discussions on software product lines in general and feature modeling in particular. The results provide a historical perspective, an overview on commonly addressed scopes, and a classification of discussed topics and problems. Moreover, our findings are interesting to understand the practical impact of software-product-line techniques outside of well-analyzed case studies, to support lectures by identifying regularly asked questions, and to scope tool development based on reported technical problems.

scholarly journals Comprehensive Retrospection of Literature Reported Works of Community Question Answering Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 1904-1917
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Community ◽  
Question Answering ◽  
Hybrid Approach ◽  
Open Educational Resources ◽  
Stack Overflow ◽  
Community Question Answering ◽  
Question Answering Systems ◽  
Answer Quality

Community question answering CQA) systems are rapidly gaining attention in the society. Several researchers have actively engaged in improving the theories associated with question answering (QA) systems. This paper reviews the literature reported works on question answering QA systems. In this paper, we discuss on the early contributions on QA systems along with their present and future scope. We have categorized the literature reported works into 20 subgroups according to their significance and relevance. The works in each group will be brought out along with their inter-relevance. Finding the question and answer quality is the prime challenge almost addressed by many researchers. Modeling similar questions, identifying experts in prior and understanding seeker satisfaction also considered as potential challenges. Researchers at the most have done experimentations on popular CQAs like Yahoo! Answers, Wiki Answers, Baidu Knows, Brianly, Quora, Pubmed and Stack Overflow respectively. Machine learning, probabilistic modeling, deep learning and hybrid approach of solving show profound significance in addressing various challenges encounter with QA systems. Today the paradigm of CQA systems took the shift by serving as Open Educational Resources to learning community

Revealing Learner Interests through Topic Mining from Question-Answering Data

2017 ◽  
Vol 15 (2) ◽  
pp. 18-32 ◽  
Author(s):  
Yijie Dun ◽  
Na Wang ◽  
Min Wang ◽  
Tianyong Hao
Keyword(s):  
Question Answering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Svm Classifier ◽  
Named Entity ◽  
Community Question Answering ◽  
Question Answering Systems ◽  
Text Features ◽  
Real Community ◽  
Topic Mining

In a question-answering system, learner generated content including asked and answered questions is a meaningful resource to capture learning interests. This paper proposes an approach based on question topic mining for revealing learners' concerned topics in real community question-answering systems. The authors' approach firstly preprocesses all questions associated with learners. Afterwards, it analyzes each question with text features and generates a weight feature matrix using a revised TF/IDF method. In order to decrease the sparsity issue of data distribution, the authors employ three concept-mapping strategies including named entity recognition, synonym extension, and hyponym replacement. Applying an SVM classifier, their approach categorizes user questions into representative topics. Three experiments are conducted based on a TREC dataset and an actual dataset containing 1,120 questions posted by learners from a commercial question-answering community. Results demonstrate the effectiveness of the method compared with conventional classifiers as baselines.

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