Question answering (QA) helps one go beyond traditional keywords-based querying and retrieve information in more precise form than given by a document or a list of documents. Several community-based QA (CQA) services have emerged allowing information seekers pose their information need as questions and receive answers from their fellow users. A question may receive multiple answers from multiple users and the asker or the community can choose the best answer. While the asker can thus indicate if he was satisfied with the information he received, there is no clear way of evaluating the quality of that information. We present a study to evaluate and predict the quality of an answer in a CQA setting. We chose Yahoo! Answers as such CQA service and selected a small set of questions, each with at least five answers. We asked Amazon Mechanical Turk workers to rate the quality of each answer for a given question based on 13 different criteria. Each answer was rated by five different workers. We then matched their assessments with the actual asker’s rating of a given answer. We show that the quality criteria we used faithfully match with asker’s perception of a quality answer. We furthered our investigation by extracting various features from questions, answers, and the users who posted them, and training a number of classifiers to select the best answer using those features. We demonstrate a high predictability of our trained models along with the relative merits of each of the features for such prediction. These models support our argument that in case of CQA, contextual information such as a user’s profile, can be critical in evaluating and predicting content quality.

Abstract Recent work in automatic question answering has called for question taxonomies as a critical component of the process of machine understanding of questions. There is a long tradition of classifying questions in library reference services, and digital reference services have a strong need for automation to support scalability. Digital reference and question answering systems have the potential to arrive at a highly fruitful symbiosis. To move towards this goal, an extensive review was conducted of bodies of literature from several fields that deal with questions, to identify question taxonomies that exist in these bodies of literature. In the course of this review, five question taxonomies were identified, at four levels of linguistic analysis.

Abstract This article seeks to shift the literature on chat‐based reference services beyond the current spate of case studies and discussions of emerging standards and best practices in providing chat‐based reference, to a higher level of discussion on the creation and discussion of theoretical frameworks to unite these standards and practices. The article explores the various steps in the process of providing synchronous, chat‐based reference, as well as issues involved in providing such service at each step. The purpose of this exploration is twofold: First, this article presents some open research questions at each step in the process of providing chat‐based reference service. Second, the entire process of providing chat‐based reference is viewed as a whole, and a model of the provision of chat‐based reference service is developed at a high level of abstraction. It is hoped that this model may serve as a conceptual framework for future discussions of and development of applications for chat‐based reference.

This article examines the entry of the major academic publishing houses into World Wide Web (Web) publishing. The study identified that during 1997, traditional academic publishers made significant commitments to putting tables of contents, abstracts, and the full-text of their print journals on the Web. At the same time, new services and organizations emerged that could ultimately compete with, or eliminate, the need for certain segments of the industry. The authors suggest that these early experiments in Web publishing began unevenly with areas that needed improvement. The article concludes with a discussion about the implications of the traditional academic publisher's presence on the Web. © 1998 John Wiley & Sons, Inc.