Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

  title={Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems},
  author={Binbin Jin and Enhong Chen and Hongke Zhao and Zhenya Huang and Qi Liu and Hengshu Zhu and Shui Yu},
  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  • Binbin Jin, Enhong Chen, +4 authors Shui Yu
  • Published 1 June 2019
  • Computer Science, Mathematics
  • IEEE Transactions on Systems, Man, and Cybernetics: Systems
In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multifacet domain effects in CQA are still underexplored. In this paper, we propose a unified model, enhanced attentive recurrent neural network (EARNN), for both answer selection and answer ranking… 
Learning the Compositional Visual Coherence for Complementary Recommendations
A novel Content Attentive Neural Network (CANN) is proposed to model the comprehensive compositional coherence on both global contents and semantic contents and is optimized in a novel compositional optimization strategy.


Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning
This paper proposes a novel asymmetric multi-faceted ranking network learning framework for question answering by exploiting both answers’ relative quality rank to given questions and the answerers’ following relations in CQA sites.
Word Embedding Based Correlation Model for Question/Answer Matching
A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text.
Question-answer topic model for question retrieval in community question answering
A novel Question-Answer Topic Model (QATM) is proposed to learn the latent topics aligned across the question-answer pairs to alleviate the lexical gap problem, with the assumption that a question and its paired answer share the same topic distribution.
Selecting Best Answer: An Empirical Analysis on Community Question Answering Sites
A model is developed for selecting best answer for the question asked on the CQA site that takes both question-answer and answerers' data into account, which gives an insight view into the answers given by the experts that is more likely to be selected as the best answer.
Learning the Latent Topics for Question Retrieval in Community QA
This paper proposes a topic model incorporated with the category information into the process of discovering the latent topics in the content of questions and combines the semantic similarity based latent topics with the translation-based language model into a unified framework for question retrieval.
Data-Driven Answer Selection in Community QA Systems
A novel scheme to rank answer candidates via pairwise comparisons using a novel model to jointly incorporate positive, negative, and neutral training samples guided by data-driven observations is presented.
A Comprehensive Survey and Classification of Approaches for Community Question Answering
A review of 265 articles published between 2005 and 2014, which were selected from major conferences and journals are reviewed to propose a framework that defines descriptive attributes of CQA approaches and introduce a classification of all approaches with respect to problems they are aimed to solve.
A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering
The proposed method uses a stacked bidirectional Long-Short Term Memory network to sequentially read words from question and answer sentences, and then outputs their relevance scores, which outperforms previous work which requires syntactic features and external knowledge resources.
Evaluating and predicting answer quality in community QA
A study to evaluate and predict the quality of an answer in a CQA setting and supports the argument that contextual information such as a user's profile, can be critical in evaluating and predicting content quality.
Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow
The authors' L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums, and was able to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings.