Expert finding in community question answering: a review

Yuan, Sha; Zhang, Yu; Tang, Jie; Hall, Wendy; Cabotà, Juan Bautista

doi:10.1007/s10462-018-09680-6

Cited by 79 publications

(37 citation statements)

References 72 publications

(48 reference statements)

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“…R. Polikar reviewed conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined [38]. In 2019, Sha Yuan et al used innovative diagrams to clarify several important concepts of ensemble learning, and found that ensemble models with several specific single models can further boost the performance [39].…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Multi-Label Classification of Fundus Images With EfficientNet

et al. 2020

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Convolutional neural network (CNN) has achieved remarkable success in the field of fundus images due to its powerful feature learning ability. Computer-aided diagnosis can obtain information with reference value for doctors in clinical diagnosis or screening through proper processing and analysis of fundus images. However, most of the previous studies have focused on the detection of a certain fundus disease, and the simultaneous diagnosis of multiple fundus diseases still faces great challenges. We propose a multi-label classification ensemble model of fundus images based on CNN to directly detect one or more fundus diseases in the retinal fundus images. Every single model consists of two parts. The first part is a feature extraction network based on EfficientNet, and the second part is a custom classification neural network for multi-label classification problems. Finally, the output probabilities of different models are fused as the final recognition result. And it was trained and tested on the data set provided by ODIR 2019 (Peking University International Competition on Ocular Disease Intelligent Recognition). The experimental results show that our model can be trained on fewer data sets and get good results.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Multi-Label Classification of Fundus Images With EfficientNet

et al. 2020

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“…Li, Jiang, Sun, and Wang (2019) developed a heterogeneous information network embedding algorithms and a novel Long Short‐Term Memory model to embed the information of question text, raiser and answerer into a unified representation. To facilitate the academic progress of the issue, Al‐Taie, Kadry, and Obasa (2018), Wang, Huang, Yao, Benatallah, and Dong (2018) and Yuan, Zhang, Tang, Hall, and Cabotà (2020) concluded the taxonomy and the state‐of‐the‐art of expert finding, and pointed out the possible direction for further research.…”

Section: Related Workmentioning

confidence: 99%

Domain problem‐solving expert identification in community question answering

Tang

et al. 2020

Expert Systems

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Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining welldesigned expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian-Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.

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“…This topic has been extensively studied before. Three comprehensive surveys can be found in [10]- [12]. Earlier works on question routing are mainly based on the past answering activities of the users.…”

Section: Related Work a Question Routingmentioning

confidence: 99%

Helping the Ineloquent Farmers: Finding Experts for Questions With Limited Text in Agricultural Q&A Communities

et al. 2020

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Nowadays, hundreds of thousands of farmers in China seek online in agricultural Q&A communities, such as Farm-Doctor, for agricultural advice. As in many other Q&A communities, the key design issue is to find experts to provide timely and suitable answers. State-of-the-art approaches often rely on extracting topics from the question texts, however, the major challenge here is that questions in agricultural Q&A communities often contain limited textual information. To solve this problem, in this article, we conduct an extensive measurement on Farm-Doctor, which consists of over 690 thousand questions and over 3 million answers, and we model Farm-Doctor as a heterogeneous information network that incorporates rich side information. We propose a novel approach based on graph neural network to accurately recommend for each question the users that are highly likely to answer it. With an average income of fewer than 6 dollars a day, our method helps these less eloquent farmers with their cultivation and hopefully provides a way to improve their lives. INDEX TERMS Question and answering, question routing, network representation learning.

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Expert finding in community question answering: a review

Cited by 79 publications

References 72 publications

Multi-Label Classification of Fundus Images With EfficientNet

Multi-Label Classification of Fundus Images With EfficientNet

Domain problem‐solving expert identification in community question answering

Helping the Ineloquent Farmers: Finding Experts for Questions With Limited Text in Agricultural Q&A Communities

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