A framework to predict the quality of answers with non-textual features

By Jiwoon Jeon, W. Bruce Croft, Joon Ho Lee, Soyeon Park. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006

413 citations recorded by the source corpus.

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/46dbcee23edc8385921749f6710f480440a61375

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Citations in the corpus

Analysis of the Perceived Value of Online Tourism Reviews: Influence of Readability and Reviewer Characteristics (2016, background): “Review length will influence perceived helpfulness significantly by exploring the effect of length according to machine learning approaches such as support vector machine, review length is found to influence perceived helpfulness significantly (Jeon et al., 2006; Kim, Pantel, Chklovski,& Pennacchiot”
Finding high-quality content in social media (2008, methodology): “[17] extracted a set of features from a sample of answers in Naver, a Korean question/answering portal similar to Yahoo! Answers.”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, background): “From a business perspective, consumer product reviews are most influential if they affect product sales and the online behavior of users of the word-of-mouth forum.”
Finding the right facts in the crowd: factoid question answering over social media (2008, background): “[11] built a model for answer quality based on features derived from the specific answer being analyzed.”
A Comprehensive Survey and Classification of Approaches for Community Question Answering (2016, methodology): “The first approaches employed sets of automatically extracted features, either non-textual [Jeon et al. 2006] or a combination of textual and non-textual [Blooma ACM Transactions on the Web, Vol. 10, No. 3, Article 18, Publication date: August 2016.”
Learning to recognize reliable users and content in social media with coupled mutual reinforcement (2009, background): “[9] built a model for answer quality based on features derived from the specific answer being analyzed.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “Finally, others evaluate the quality of user generated content, including answer quality [194, 212, 35, 110] and question quality [39, 136].”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “The second is the perspective of information retrieval wher e a question is viewed as a “query” and answers could be thought o f as “results” [15, 8, 16, 2, 9].”
Retrieval models for question and answer archives (2008, background): “Apart from Jeon’s previous work ([6, 5, 4], there has been other work on retrieval from FAQ data, which is very similar to Q&A data.”