Confucius and its intelligent disciples

Si, Xiance; Chang, Edward Yi; Gyongyi, Zoltan; Sun, Maosong

doi:10.14778/1920841.1921025

Cited by 25 publications

(15 citation statements)

References 14 publications

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“…Thus, this paper asks such research question: Can we automatically identify qweets from tweets? We know that microblog provides an instant message publishing service, so identifying these questions automatically will help question askers get answers efficiently through approaches such as question routing and automatic question answering system [5]. Identifying qweets also lays a foundation for question analysis (such as classification and clustering) in the microblog environment.…”

Section: Introductionmentioning

confidence: 99%

Question identification on twitter

Si²,

Lyu

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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In this paper, we investigate the novel problem of automatic question identification in the microblog environment. It contains two steps: detecting tweets that contain questions (we call them "interrogative tweets") and extracting the tweets which really seek information or ask for help (so called "qweets") from interrogative tweets. To detect interrogative tweets, both traditional rule-based approach and state-of-the-art learning-based method are employed. To extract qweets, context features like short urls and Tweetspecific features like Retweets are elaborately selected for classification. We conduct an empirical study with sampled one hour's English tweets and report our experimental results for question identification on Twitter.

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Section: Introductionmentioning

confidence: 99%

Question identification on twitter

Si²,

Lyu

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

Self Cite

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“…The synergy between the two paradigms have been already exploited by services like Google Confucius [22], where users are redirected to a social search platform for some query categories. Another social approach to deal with complex query activity has been adopted by Yahoo!…”

Section: B Social Searchmentioning

confidence: 99%

Tagging relations to achieve complex search goals

Schifanella

Aiello

Ruffo

2011

2011 7th International Conference on Next Generation Web Services Practices

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Abstract-Social search is having a flourishing success for its effectiveness in retrieving high quality information useful to achieve complex search goals. Surprisingly, the potential of the social paradigm at the basis of collaborative tagging in satisfying complex search intents has been unexplored so far. We propose an extended model of folksonomies that allows to compose and tag complex user-defined relations among items, users, and tags. We show that this model offers several means to fulfill complex search tasks that are hard to be achieved by other existing services. Furthermore, we support the validity of our approach through a data-driven analysis on Flickr photosets and we present an online portal that provides this new user experience.

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“…Previous approaches [6,24] focus on learning to rank different objects in Q&A sites under supervised or semi-supervised settings, which explicitly or implicitly assume the availability of some prior knowledge. However, with large volumes of new questions, answers and users, extracting supervised information from Q&A sites can be very expensive and time consuming.…”

Section: Introductionmentioning

confidence: 99%

NCR

Zhang

Kong

Luo

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Question-and-answer (Q&A) websites, such as Yahoo! Answers, Stack Overflow and Quora, have become a popular and powerful platform for Web users to share knowledge on a wide range of subjects. This has led to a rapidly growing volume of information and the consequent challenge of readily identifying high quality objects (questions, answers and users) in Q&A sites. Exploring the interdependent relationships among different types of objects can help find high quality objects in Q&A sites more accurately. In this paper, we specifically focus on the ranking problem of coranking questions, answers and users in a Q&A website. By studying the tightly connected relationships between Q&A objects, we can gain useful insights toward solving the coranking problem. However, co-ranking multiple objects in Q&A sites is a challenging task: a) With the large volumes of data in Q&A sites, it is important to design a model that can scale well; b) The large-scale Q&A data makes extracting supervised information very expensive. In order to address these issues, we propose an unsupervised Networkbased Co-Ranking framework (NCR) to rank multiple objects in Q&A sites. Empirical studies on real-world Yahoo! Answers datasets demonstrate the effectiveness and the efficiency of the proposed NCR method.

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Confucius and its intelligent disciples

Cited by 25 publications

References 14 publications

Question identification on twitter

Question identification on twitter

Tagging relations to achieve complex search goals

NCR

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