Matches in ScholarlyData for { ?s ?p ?o. }
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- 5 creator man-lan.
- 5 creator yue-lu.
- 5 creator zhengyu-niu.
- 5 creator zhimin-zhou.
- 5 type InProceedings.
- 5 label "Exploiting user profile information for answer ranking in cQA".
- 5 sameAs 5.
- 5 abstract "Answer ranking is very important for cQA services due to the high variance in the quality of answers. Most existing works in this area focus on using various features or employing machine learning techniques to address this problem. Only a few of them noticed and involved user profile information in this particular task. In this work, we assume the close relationship between user profile information and the quality of their answers under the ground truth that user information records the user behaviors and histories as a summary. Thus, we exploited the effectiveness of three categories of user profile information, i.e. engagement-related, authority-related and level-related, on answer ranking in cQA. Different from previous work, we only employed the information which is easy to extract without any limitations, such as user privacy. Experimental results on Yahoo! Answers manner questions showed that our system by using the user profile information achieved comparable or even better results over the state-of-the-art baseline system. Moreover, we found that the picture existence of a user in cQA community contributed more than other information in the answer ranking task.".
- 5 hasAuthorList authorList.
- 5 isPartOf proceedings.
- 5 keyword "Answer ranking".
- 5 keyword "Commnuity-Question Answering".
- 5 keyword "User profile infromation".
- 5 title "Exploiting user profile information for answer ranking in cQA".
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- item-1 hasContent zhimin-zhou.
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- item-2 hasContent man-lan.
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- item-3 hasContent zhengyu-niu.
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- item-4 hasContent yue-lu.
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- 6 creator long-chen.
- 6 type InProceedings.
- 6 label "Understanding User Intent in Community Question Answering".
- 6 sameAs 6.
- 6 abstract "Community Question Answering (CQA) services, such as Yahoo! Answers, are specifically designed to address the innate limitation of Web search engines by helping users obtain information from a community. Understanding the user intent of questions would enable a CQA system identify similar questions, find relevant answers, and recommend potential answerers more effectively and efficiently. In this paper, we propose to classify questions into three categories according to their underlying user intent: subjective, objective, and social. In order to identify the user intent of a new question, we build a predictive model through machine learning based on both text and metadata features. Our investigation reveals that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in co-training - a semi-supervised learning framework - to make use of a large amount of unlabelled questions, in addition to the small set of manually labelled questions, for enhanced question classification. The preliminary experimental results show that co-training works significantly better than simply pooling these two types of features together.".
- 6 hasAuthorList authorList.
- 6 isPartOf proceedings.
- 6 keyword "Co-training".
- 6 keyword "Community Question Answering".
- 6 keyword "Semi-Supervised Learning".
- 6 keyword "User Intent".
- 6 title "Understanding User Intent in Community Question Answering".
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- item-1 hasContent long-chen.
- 8 creator atsushi-otsuka.
- 8 creator noriko-kando.
- 8 creator tetsuji-satoh.
- 8 creator yohei-seki.
- 8 type InProceedings.
- 8 label "QAque: Faceted Query Expansion techniques for Exploratory Search using Community QA Resources".
- 8 sameAs 8.
- 8 abstract "Recently, query suggestions are quite useful in web searches. Most of them provide additional and correct terms based on the initial query entered by users. However, users can not be satisfied with them, when user has ambiguous or diverse information requests. In those cases, faceted query expansions along with their usage are quite efficient.In this paper, faceted query expansion methods using Community QuestionAnswering(CQA) resources are proposed. CQA is one of the social networkservice(SNS) which aims to share user knowledge. In a CQA site, users can post their questions in a suitable category. And others pick up and answer the questions according to the category framework. Thus, the "category" in CQA makes "facet" of query expansion. In addition, the season of question posting plays a important role in understanding its context. Thus, the "seasonality" makes another "facet" of query expansion.Therefore, we implement the two-dimensional faceted query expansion methods based on the results of LDA( Latent Dirichlet Allocation ) analysis to the CQA resources. Moreover, the question-articles deriving query expansion will be provided for choosing appropriate terms by users.Our sophisticated evaluations using actual and long-term CQA resources, such as "Yahoo! CHIEBUKURO", demonstrate that most part of CQA questions are posted in periodicity and bursts.".
- 8 hasAuthorList authorList.
- 8 isPartOf proceedings.
- 8 keyword "Community QA".
- 8 keyword "category".
- 8 keyword "faceted search".
- 8 keyword "latent dirichlet allocation".
- 8 keyword "query expansion".
- 8 keyword "seasonality".
- 8 title "QAque: Faceted Query Expansion techniques for Exploratory Search using Community QA Resources".
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- item-1 hasContent atsushi-otsuka.
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- item-2 hasContent yohei-seki.
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- item-3 hasContent noriko-kando.
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- item-4 hasContent tetsuji-satoh.
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- 9 creator evangelos-milios.
- 9 creator fatemeh-riahi.
- 9 creator mahdi-shafiei.
- 9 creator zainab-zolaktaf.
- 9 type InProceedings.
- 9 label "Finding Expert Users in Community Question Answering".
- 9 sameAs 9.
- 9 abstract "Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth while offering new opportunities, puts forward new challenges. In most CQA implementations, there is little effort in directing new questions to the right group of experts. This means that experts are not be provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.".