Discovering high quality answers in community question answering archives using a hierarchy of classifiers

Toba, Hapnes; Ming, Zhaoyan; Adriani, Mirna; Chua, Tat-Seng

doi:10.1016/j.ins.2013.10.030

Cited by 99 publications

(54 citation statements)

References 45 publications

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“…A hybrid hierarchy-of-classifiers framework for finding the quality answers in yahoo answers is proposed by Toba,Hapens [8] . Before analyzing the different answers to a given question, the question is analyzed first.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Question –Answering System

Saini¹,

Yadav²

2017

IJECS

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Abstract-Question-Answering(QA) is a new research area/ region in the field of Information science which comes into focus in last few decade. The present study is undertaken to survey about the QA system.The study in this paper will provide the guidelines to the researchers, scholars and practitioners of computer engineering. In this paper, the study is undertaken by planning, conducting, evaluating and reporting the literature review from the past years. A Question -Answering system consists of three core components i.e. question classification ,information retrieval and answer extraction module. This paper aims at giving an overview in this field, evaluating the current and emerging status and visualizing the future scope and trends.

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Section: Related Workmentioning

confidence: 99%

A Survey on Question –Answering System

Saini¹,

Yadav²

2017

IJECS

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“…Zhou et al [15] proposed a joint learning method to measure quality of an answer. Toba et al [16] examined a question type to select a right answer. Paul et al [17] studied how people evaluate quality of an answer.…”

Section: Temporal Featuresmentioning

confidence: 99%

Detecting experts on Quora: by their activity, quality of answers, linguistic characteristics and temporal behaviors

Patil

Lee

2015

Soc. Netw. Anal. Min.

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Quora is a fast growing social Q&A site where users create and answer questions, and identify the best answers by upvotes and downvotes with crowd wisdom. Unfortunately, little is known about properties of experts and non-experts and how to detect experts in general topics or a specific topic. To fill the gaps, in this paper we (i) analyze behaviors of experts and non-experts in five popular topics; (ii) propose user activity features, quality of answer features, linguistic features and temporal features to identify distinguishing patterns between experts and non-experts; and (iii) develop statistical models based on the features to automatically detect experts. Our experimental results show that our classifiers effectively identify experts in general topics and a specific topic, achieving up to 97% accuracy and 0.987 AUC. In this paper, we are investigating and identifying potential experts who are providing the best solutions to the questioner needs. We have considered several techniques in identifying user as an expert or non-expert. We have targeted the most followed topics in Quora and finally came up with five topics: Mathematics, Politics, Technology, Sports and Business.We then crawled the user profiles who are following these topics. Each topic dataset has many special features. Our research indicates that experts are quite different from normal users and tend to produce high quality answers to as many questions as possible to gain their reputation. After evaluation, we got a limited number of experts who have potential expertise in specific fields, achieving up to 97% accuracy and 0.987 AUC.

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“…In (Toba et al, 2014) a 2-layer classification method has been proposed. The first layer just tries to find the type of the question and the second layer uses the result of the first layer to find the best answer of the question.…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, we tried to extract and use reputational features as well. Some of the shallow features used, are the same as shallow 1 http://www.qatarLiving.com/forum features in (Gkotsis et al, 2014;Toba et al, 2014) and some other features are from the available information in the corpus like: Creation Date, Category, and Question Type. It was assumed Questioners comments can be so informative, experiments show that, features which are using this fact can be so effective.…”

Section: Featuresmentioning

confidence: 99%

Shiraz: A Proposed List Wise Approach to Answer Validation

Alashty

Rahmani

Roostaee

et al. 2015

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Answer Validation is an important step in Automatic Question Answering systems and nowadays by spreading Community Question Answering systems it is known as an important task by itself. Previous works just considered it as a binary classification problem in which they try to find the best answer among all the candidate answers for a question. Accordingly, they do not consider the possible unique information which may have been included other answers. This can be considered by having a multiclass label classification problem, it is not only able to find the best answer but also can find "potentially good", "bad", and etc. answers too. By doing so, it is fully expected to extract and rate all the necessary information from existing candidates to help questioner to find the best and general answer for his question. This work tries to consider some features which are gained from importance of comments of the questioner. Finally, by using a good classifier, we try to overcome this problem. The designed system participated in subtask A of the Semeval-2015 Task 3. The primary submission ranked at the 5th and 7th places in four class label and three class label evaluation, accordingly.

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Discovering high quality answers in community question answering archives using a hierarchy of classifiers

Cited by 99 publications

References 45 publications

A Survey on Question –Answering System

A Survey on Question –Answering System

Detecting experts on Quora: by their activity, quality of answers, linguistic characteristics and temporal behaviors

Shiraz: A Proposed List Wise Approach to Answer Validation

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