Query completion in community-based Question Answering search

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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FINDING EXPERTS IN COMMUNITY BASED QUESTION-ANSWERING SERVICES

International Journal of Advance Engineering and Research Development ◽

10.21090/ijaerd.04013 ◽

2017 ◽

Vol 4 (01) ◽

Keyword(s):

Question Answering ◽

Community Based

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Mining Community-Based Top-k Experts and Learners in Online Question Answering Systems

Advances in Intelligent Systems and Computing - Proceedings of the Second International Conference on Computer and Communication Technologies ◽

10.1007/978-81-322-2517-1_50 ◽

2015 ◽

pp. 519-526

Author(s):

S Rao Chintalapudi ◽

M. H. M. Krishna Prasad

Keyword(s):

Question Answering ◽

Community Based ◽

Mining Community ◽

Question Answering Systems

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Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.3301395 ◽

2019 ◽

Vol 33 ◽

pp. 395-402

Author(s):

Xiao Yang ◽

Madian Khabsa ◽

Miaosen Wang ◽

Wei Wang ◽

Ahmed Hassan Awadallah ◽

...

Keyword(s):

Question Answering ◽

State Of The Art ◽

Classification Problem ◽

Classification Model ◽

Training Set ◽

Community Based ◽

Multi Scale ◽

Adversarial Training ◽

Source Of Information ◽

Different Levels

Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue. We employ a generative model to iteratively sample a subset of challenging negative samples to fool our classification model. Both models are alternatively optimized using REINFORCE algorithm. The proposed method is completely different from previous ones, where negative samples in training set are directly used or uniformly down-sampled. Further, we propose using Multi-scale Matching which explicitly inspects the correlation between words and ngrams of different levels of granularity. We evaluate the proposed method on SemEval 2016 and SemEval 2017 datasets and achieves state-of-the-art or similar performance.

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