Formulation of a hybrid expertise retrieval system in community question answering services

Kundu, Dipankar; Mandal, Deba Prasad

doi:10.1007/s10489-018-1286-z

Cited by 24 publications

(7 citation statements)

References 60 publications

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“…Also, we observe that some models tend to mix different techniques among DLM, VSM, and/or GM [29]. For example, AuthorRank [30] combined a generative probabilistic DLM and a PageRank-like GM based on community engagement of expert candidates.…”

Section: Related Workmentioning

confidence: 99%

“…The work [31] combined a cluster-based language model and a VSM for finding experts in question and answer communities. The authors [29] proposed a complex model for community question answering using a variation of the DLM [6] and a HITS-based GM (the HITS algorithm on a competition based expertise network [32]), where the scores from these models were linearly combined to rank experts given a question. The work [33] used the Dempster-Shafer combination theory to combine the DLM [6] and a graph algorithm that analyses a social interaction of experts.…”

Section: Related Workmentioning

confidence: 99%

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ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

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Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder, a new ensemble model for expert finding, that integrates a novel N -gram vector space model, denoted as nVSM, and a graph-based model, denoted as µCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N -gram words, and ExpFinder incorporates nVSM into µCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To solve the cold-start problems, Wan et al [2018] and Zhao et al [2014] exploited knowledge from multiple sources to support question answering. A hybrid system to retrieval expertise to help to answer questions is also proposed [Kundu and Mandal 2019]. Liu et al [2008] suggested that users can reuse the best answers from similar questions as search result snippets, and highlighted the effectiveness of applying automatic summarization techniques to summarize answers.…”

Section: Interactive Searchmentioning

confidence: 99%

Towards Question-based High-recall Information Retrieval

Zou

Kanoulas

2020

ACM Trans. Inf. Syst.

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While continuous active learning algorithms have proven effective in finding most of the relevant documents in a collection, the cost for locating the last few remains high for applications such as Technology-assisted Reviews (TAR). To locate these last few but significant documents efficiently, Zou et al. [2018] have proposed a novel interactive algorithm. The algorithm is based on constructing questions about the presence or absence of entities in the missing relevant documents. The hypothesis made is that entities play a central role in documents carrying key information and that the users are able to answer questions about the presence or absence of an entity in the missing relevance documents. Based on this, a Sequential Bayesian Search-based approach that selects the optimal sequence of questions to ask was devised. In this work, we extend Zou et al. [2018] by (a) investigating the noise tolerance of the proposed algorithm; (b) proposing an alternative objective function to optimize, which accounts for user “erroneous” answers; (c) proposing a method that sequentially decides the best point to stop asking questions to the user; and (d) conducting a small user study to validate some of the assumptions made by Zou et al. [2018]. Furthermore, all experiments are extended to demonstrate the effectiveness of the proposed algorithms not only in the phase of abstract appraisal (i.e., finding the abstracts of potentially relevant documents in a collection) but also finding the documents to be included in the review (i.e., finding the subset of those relevant abstracts for which the article remains relevant). The experimental results demonstrate that the proposed algorithms can greatly improve performance, requiring reviewing fewer irrelevant documents to find the last relevant ones compared to state-of-the-art methods, even in the case of noisy answers. Further, they show that our algorithm learns to stop asking questions at the right time. Last, we conduct a small user study involving an expert reviewer. The user study validates some of the assumptions made in this work regarding the user’s willingness to answer the system questions and the extent of it, as well as the ability of the user to answer these questions.

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“…Much research has been conducted to solve the problem of information overload and to help community users find the needed Q&A documents. These studies focused on finding high-quality answers (Roy et al , 2018; Elalfy et al , 2018), retrieving questions (Chen et al , 2018), finding suitable experts to answer posted questions (Tang and He, 2016; Wang et al , 2017; Kundu and Mandal, 2019), estimating question difficulty (Sun et al , 2018; Huna et al , 2016), recommending Q&A documents for users (Liu et al , 2014; Fu, 2019) and identifying complementary Q&A documents (Liu et al , 2015). These achievements are effective at easing the problems inherent to CQA.…”

Section: Introductionmentioning

confidence: 99%

Explanatory Q&A recommendation algorithm in community question answering

et al. 2020

DTA

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PurposeIn community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.Design/methodology/approachIn the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.FindingsThe GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.Research limitations/implicationsThe proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.Originality/valueA novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

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Formulation of a hybrid expertise retrieval system in community question answering services

Cited by 24 publications

References 60 publications

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Towards Question-based High-recall Information Retrieval

Explanatory Q&A recommendation algorithm in community question answering

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