Community Question Answering Services Research Articles

EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING

Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.

Evolution of Experts in Question Answering Communities

Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.

The Implementation of Question Answer System Using Deep Learning

Question-answer systems are referred to as advanced systems that can be used to provide answers to the questions which are asked by the user. The typical problem in natural language processing is automatic question-answering. The question-answering is aiming at designing systems that can automatically answer a question, in the same way as a human can find answers to questions. Community question answering (CQA) services are becoming popular over the past few years. It allows the members of the community to post as well as answer the questions. It helps users to get information from a comprehensive set of questions that are well answered. In the proposed system, a deep learning-based model is used for the automatic answering of the user’s questions. First, the questions from the dataset are embedded. The deep neural network is trained to find the similarity between questions. The best answer for each question is found as the one with the highest similarity score. The purpose of the proposed system is to design a model that helps to get the answer of a question automatically. The proposed system uses a hierarchical clustering algorithm for clustering the questions.

Time-aware hybrid expertise retrieval system in community question answering services

This paper introduces a time-aware hybrid expertise retrieval (TaHER) system for community question answering (CQA) services. It comprises of a text-based part and a network-based part. The text-based part makes use of the textual and the temporal information associated with questions and answers. Moreover, it assesses the recent interests and the activities of answerers. For a given question, it determines the knowledge of each answerer and identify active answerers with adequate knowledge. The network-based part is composed of several period-dependent networks. It uses the relationships among the answerers along with temporal information. Next, it applies a link analysis technique on the networks to determine the time-aware authority of each answerer in the community. We, nonetheless, propose a fusion strategy for combining the offshoots of these two parts. Using 5 performance measures, TaHER system is compared with 20 state-of-the-art algorithms on 4 real-world datasets. According to our experiments, in 93.75% (375 out of 400) cases, the proposed approach outperforms the comparing approaches. We also experimentally validate the importance of each assumption used by us.

Evaluating community question-answering websites using interval-valued intuitionistic fuzzy DANP and TODIM methods

As important knowledge-sharing platforms, community question-answering (CQA) websites have attracted the attention of many users. An evaluation of the quality of CQA websites can assist users in selecting high-quality CQA websites and can help the operators of CQA websites achieve the most beneficial improvements. An approach to the evaluation of CQA websites is proposed in this study. First, a CQA service quality model is constructed to evaluate the quality of CQA websites based on the E-SERVQUAL model. A novel multicriteria decision-making (MCDM) model is proposed to address the ratings. In the model, uncertainty and consistency are combined in an interval-valued intuitionistic fuzzy (IVIF) environment to determine two kinds of expert weights. The DANP method, along with experts’ weights, is extended to the IVIF environment to derive the criteria weights. The IVIF TODIM method, along with experts’ weights, is used to rank the CQA websites in terms of quality. Finally, the proposed approach is applied to evaluate the quality of five CQA websites. The experimental results of the application, along with sensitivity analysis and comparative analysis, demonstrate the feasibility and practicality of the proposed approach.

Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis.

BackgroundDespite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited.ObjectiveThe aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information–seeking activities on the internet.MethodsSearch and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted.ResultsSignificant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, “hypothyroidism,” “ulcerative colitis”) highly ranked in search frequency. Similarly, “pain,” “slight fever,” and “numbness” were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man –1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including “abdominal pain” (10-20 years), “plasma cells” and “inflammatory findings” (20-30 years), “DM” (diabetes mellitus; 30-40 years), “abnormal shadow” and “inflammatory findings” (40-50 years), “hypertension” and “abnormal shadow” (50-60 years), and “lung cancer” and “gastric cancer” (60-70 years).ConclusionsSearch and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as “hypothyroidism” and “ulcerative colitis” had high search frequencies, whereas those related to symptoms such as “pain,” “slight fever,” and “numbness” had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as “ulcerative colitis,” which has an incidence of less than 0.1% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: “cancer”; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and “death”). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged.

Assessing the quality of answers autonomously in community question–answering

Community question–answering (CQA) has become a popular method of online information seeking. Within these services, peers ask questions and create answers to those questions. For some time, content repositories created through CQA sites have widely supported general-purpose tasks; however, they can also be used as online digital libraries that satisfy specific needs related to education. Horizontal CQA services, such as Yahoo! Answers, and vertical CQA services, such as Brainly, aim to help students improve their learning process via Q&A exchanges. In addition, Stack Overflow—another vertical CQA—serves a similar purpose but specifically focuses on topics relevant to programmers. Receiving high-quality answer(s) to a posed CQA query is a critical factor to both user satisfaction and supported learning in these services. This process can be impeded when experts do not answer questions and/or askers do not have the knowledge and skills needed to evaluate the quality of the answers they receive. Such circumstances may cause learners to construct a faulty knowledge base by applying inaccurate information acquired from online sources. Though site moderators could alleviate this problem by surveying answer quality, their subjective assessments may cause evaluations to be inconsistent. Another potential solution lies in human assessors, though they may also be insufficient due to the large amount of content available on a CQA site. The following study addresses these issues by proposing a framework for automatically assessing answer quality. We accomplish this by integrating different groups of features—personal, community-based, textual, and contextual—to build a classification model and determine what constitutes answer quality. We collected more than 10 million educational answers posted by more than 3 million users on Brainly and 7.7 million answers on Stack Overflow to test this evaluation framework. The experiments conducted on these data sets show that the model using random forest achieves high accuracy in identifying high-quality answers. Findings also indicate that personal and community-based features have more prediction power in assessing answer quality. Additionally, other key metrics such as F1-score and area under ROC curve achieve high values with our approach. The work reported here can be useful in many other contexts that strive to provide automatic quality assessment in a digital repository.

DiffQue

Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies that addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information, and textual content. In this article, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the network. It leverages features extracted from the network structure, metadata of users/posts, and textual description of questions and answers. Experiments on datasets obtained from two CQA sites (further divided into four datasets) with human annotated ground-truth show that DiffQue outperforms four state-of-the-art methods by a significant margin (28.77% higher F 1 score and 28.72% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue appropriately responds to the training noise, (ii) DiffQue is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can efficiently handle the “cold start” problem that may arise due to the lack of information for newly posted questions or newly arrived users.

ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph inference and analysis. Question difficulty and user expertise follow the characteristic of asymmetric transitivity. Maintaining such properties, while reducing the graph to a lower dimensional vector embedding space, has been the focus of much recent research. In this paper, we tackle the challenge of directed graph embedding with asymmetric transitivity preservation and then leverage the proposed embedding method to solve a fundamental task in CQAs: how to appropriately route and assign newly posted questions to users with the suitable expertise and interest in CQAs. The technique incorporates graph hierarchy and reachability information naturally by relying on a nonlinear transformation that operates on the core reachability and implicit hierarchy within such graphs. Subsequently, the methodology levers a factorization-based approach to generate two embedding vectors for each node within the graph, to capture the asymmetric transitivity. Extensive experiments show that our framework consistently and significantly outperforms the state-of-the-art baselines on three diverse realworld tasks: link prediction, and question difficulty estimation and expert finding in online forums like Stack Exchange. Particularly, our framework can support inductive embedding learning for newly posted questions (unseen nodes during training), and therefore can properly route and assign these kinds of questions to experts in CQAs.

Tracking user-role evolution via topic modeling in community question answering

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.

Improving question retrieval in community question answering service using dependency relations and question classification

To build an effective community question answering (cQA) service, determining ways to obtain questions similar to an input query question is a significant research issue. The major challenges for question retrieval in cQA are related to solving the lexical gap problem and estimating the relevance between questions. In this study, we first solve the lexical gap problem using a translation‐based language model (TRLM). Thereafter, we determine features and methods that are competent for estimating the relevance between two questions. For this purpose, we explore ways to use the results of a dependency parser and question classification for category information. Head‐dependent pairs are first extracted as bigram features, called dependency bigrams, from the analysis results of the dependency parser. The probability of each category is estimated using the softmax approach based on the scores of the classification results. Subsequently, we propose two retrieval models—the dependency‐based model (DM) and category‐based model (CM)—and they are applied to the previous model, TRLM. The experimental results demonstrate that the proposed methods significantly improve the performance of question retrieval in cQA services.

Learning pairwise patterns in Community Question Answering

In recent years, forums offering community Question Answering (cQA) services gained popularity on the web, as they offer a new opportunity for users to search and share knowledge. In fact, forums allow users to freely ask questions and expect answers from the community. Although the idea of receivi ng a direct, targeted response from other users is very attractive, it is not rare to see long threads of comments, where only a small portion of them are actually valid answers. In many cases users start conversations, ask for other information, and discuss about things, which are not central to the original topic. Therefore, finding the desired information in a long list of answers might be very time-consuming. Designing automatic systems to select good answers is not an easy task. In many cases the question and the answer do not share a large textual content, and approaches based on measuring the question-answer similarity will often fail. A more intriguing and promising approach would be trying to define valid question-answer templates and use a system to understand whether any of these templates is satisfied for a given question-answer pair. Unfortunately, the manual definition of these templates is extremely complex and requires a domain-expert. In this paper, we propose a supervised kernel-based framework that automatically learns from training question-answer pairs the syntactic/semantic patterns useful to recognize good answers. We carry out a detailed experimental evaluation, where we demonstrate that the proposed framework achieves state-of-the-art results on the Qatar Living datasets released in three different editions of the Community Question Answering Challenge of SemEval.

Enhancing Question Retrieval in Community Question Answering Using Word Embeddings

Community Question Answering (CQA) services have evolved into a popular way of online information seeking, where users can interact and exchange knowledge in the form of questions and answers. In this paper, we study the problem of finding historical questions that are semantically equivalent to the queried ones, assuming that the answers to the similar questions should also answer the new ones. The major challenge of question retrieval is the word mismatch problem between questions, as users can formulate the same question using different wording. Most existing methods measure the similarity between questions based on the bag-of-words (BOWs) representation capturing no semantics between words. Therefore, this study proposes to use word embeddings, which can capture semantic and syntactic information from contexts, to vectorize the questions. The questions are clustered using Kmeans to speed up the search and ranking tasks. The similarity between the questions is measured using cosine similarity based on their weighted continuous valued vectors. We run our experiments on real world data set from Yahoo! Answers in English and Arabic to show the efficiency and generality of our proposed method.

Exploiting cross-source knowledge for warming up community question answering services

Community Question Answering (CQA) services such as Yahoo! Answers, Quora and StackOverflow are collaborative platforms where users can share and exchange their knowledge explicitly by asking and answering questions. One essential task in CQA is learning topical expertise of users, which may benefit many applications such as question routing and best answers identification. One limitation of existing related works is that they only consider the warm-start users who have posted many questions or answers, while ignoring cold-start users who have few posts. In this paper, we aim to exploit knowledge from cross sources such as GitHub and StackOverflow to build up the richer views of expertise for better CQA. Inspired by the idea of Bayesian co-training, we propose a topical expertise model from the perspective of multi-view learning. Specifically, we incorporate the consistency existing among multiple views into a unified probabilistic graphic model. Comprehensive experiments on two real-world datasets demonstrate the performance of our proposed model with the comparison of some state-of-the-art ones.

Joint Implicit and Explicit Neural Networks for Question Recommendation in CQA Services

Community question answering (CQA) services have emerged as a type of popular social platforms. In the social network, experts provide knowledgeable answers to the questions in their domain of expertise, while celebrities publish influential opinions toward the topics led by some questions. Given the large amount of knowledge organized in the format of question–answers, an interesting research problem is to recommend questions to users so as to maximize their engagements with the platform. However, recommending questions in CQA services is a non-trivial task. Data sources in the CQA services are of different types. It is challenging to incorporate heterogeneous information for the recommendation task. Furthermore, data sparsity is an inherent problem in such platforms. In this paper, we propose a model that is able to jointly model both implicit and explicit information for question recommendation. The model integrates multiple data sources and addresses the problem of data heterogeneity. In the proposed model, we dynamically discover latent user groups and incorporate those hierarchical information to bridge the semantic gaps among users in the shared latent space. We evaluate the proposed model on two real-world datasets, and demonstrate that our model outperforms the state-of-the-art alternatives by a large margin. We also investigate different structures of the proposed model to study the effects of different data sources.

Convolutional neural networks for expert recommendation in community question answering

Community Question Answering (CQA) is becoming an increasingly important web service for people to search for expertise and to share their own. With lots of questions being solved, CQA have built a massive, freely accessible knowledge repository, which can provide valuable information for the broader society rather than just satisfy the question askers. It is critically important for CQA services to get high quality answers in order to maximize the benefit of this process. However, people are considered as experts only in their own specialized areas. This paper is concerned with the problem of expert recommendation for a newly posed question, which will reduce the questioners waiting time and improve the quality of the answer, so as to improve the satisfaction of the whole community. We proposean approach based on convolutional neural networks (CNN) to resolve this issue. Experimental analysis over a large real-world dataset from Stack Overflow demonstrates that our approach achieves asignificant improvement over several baseline methods.

A Multi-Objective Optimization Approach for Question Routing in Community Question Answering Services

Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online, which enables people to help each other by sharing knowledge. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality of CQA services. To address this problem, question routing has been proposed which aims at routing new questions to suitable answerers, who have both high possibility and high ability to answer the questions. In this paper, we formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can simultaneously optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are relatively short and usually attached with tags, we first propose a tagword topic model (TTM) to derive topical representations of questions. Based on TTM, we then develop features for each question-user pair, which are captured at both platform level and thread level. In particular, the platform-level features summarize the information of a user from his/her history posts in the CQA platform, while the thread-level features model the pairwise competitions of a user with others in his/her answered threads. Finally, we extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Extensive experimental results on real-world datasets show that our MLQR can outperform state-of-the-art methods in terms of both answering possibility and answer quality.

Why was this asked? Automatically recognizing multiple motivations behind community question-answering questions

Community Question Answering (cQA) services encourage their members to ask any kind of question, which later on can get multiple answers from other community fellows. The research objective of this paper is understanding, and particularly automatically detecting, what motivates community members to ask questions to their unknown peers.In so doing, we first crawled a set of cQA questions from Yahoo! Answers, each of which was manually labelled according to their multiple motivations afterwards. Thus, one of the innovative aspects of our work is exploring a wide variety of multi-label classification strategies for the automatic recognition of concurrent motivations behind cQA questions. In order to build effective models, high-dimensional feature spaces were constructed on top of assorted linguistic features, this way discovering some linguistic traits that characterize some of these combinations.Overall, our experiments reveal that multi-label classification frameworks hold a real promise for this task. More precisely, our best configuration finished with a Hamming Score of 0.71. In terms of features, our outcomes unveil that the concurrence of motivations is likely to be signalled by the complexity in writing and the distribution of entity mentions across the entire question, ergo both question titles and bodies are required to be able to recognize their confluence.

Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ( $>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.

Text Analytics for Predicting Question Acceptance Rates

Online community question answering (CQA) services have gained unprecedented popularity among users wanting to voluntarily exchange solutions without a fee. However, CQA faces two challenges: the growing volume of databases and the increasing number of questions left unanswered. This article proposes classification in text analytics as one way to predict how likely a posted question is to be answered. This involves evaluating the features that characterize the question to understand why community members are or aren't answering it. Insights from text analytics could help CQA managers guide users regarding posting etiquette, thereby retaining such services' appeal and ensuring healthy knowledge growth. This study presents a feasible solution to tackle these two problems in CQA, and does so with promising results--particularly in classification by data stream mining with accelerated swarm search feature selection.