answering doctrinal historical and social questions

New retrieval approaches using SMART: TREC 4. In Proceedings of the Joint 2013 EDBT/ICDT Conferences (EDBT’13).

Choosing the right crowd: Expert finding in social networks.

Alessandro Bozzon, Marco Brambilla, Stefano Ceri, Matteo Silvestri, and Giuliano Vesci.In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Identifying authoritative actors in question-answering forums: The case of Yahoo! Answers. Mohamed Bouguessa, Benoît Dumoulin, and Shengrui Wang.In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. Rank learning for factoid question answering with linguistic and semantic constraints. In Proceedings of the 18th International Conference on World Wide Web (Google Scholar Digital Library Learning to recognize reliable users and content in social media with coupled mutual reinforcement. Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichtein, and Hongyuan Zha.In Proceedings of the 17th International Conference on World Wide Web (Google Scholar Digital Library Finding the right facts in the crowd: Factoid question answering over social media. Jiang Bian, Yandong Liu, Eugene Agichtein, and Hongyuan Zha.In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’00). Bridging the lexical chasm: Statistical approaches to answer-finding. Berger, Rich Caruana, David Cohn, Dayne Freitag, and Vibhu O. Journal of Machine Learning Research 3, 1137-1155. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin.Studies in Computational Intelligence, Vol. In Information Retrieval and Mining in Distributed Environments. Integrating sense discrimination in a semantic information retrieval system. Pierpaolo Basile, Annalina Caputo, and Giovanni Semeraro.In Evaluation of Natural Language and Speech Tools for Italian. Super-sense tagging using support vector machines and distributional features. Linguistic Issues in Language Technologies 9, 6, 5-110. Frege in space: A program of compositional distributional semantics. Marco Baroni, Raffaela Bernardi, and Roberto Zamparelli.In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’06). Formal models for expert finding in enterprise corpora. Krisztian Balog, Leif Azzopardi, and Maarten de Rijke.In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). Competition-based networks for expert finding. Çigdem Aslay, Neil O’Hare, Luca Maria Aiello, and Alejandro Jaimes.In Proceedings of the International Conference on Information Technology and Applications. Automated question answering: Review of the main approaches. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12).

Discovering value from community activity on focused question answering sites: A case study of stack overflow. Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, and Jure Leskovec.In Proceedings of the International Conference on Web Search and Data Mining (WSDM’08). Finding high-quality content in social media. Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, and Gilad Mishne.Knowledge sharing and Yahoo Answers: Everyone knows something. Adamic, Jun Zhang, Eytan Bakshy, and Mark S. The best feature set reaches an improvement between 11% and 26% in compared to recent well-established state-of-the-art methods. We propose a novel family of distributional semantics measures that most of the time can seamlessly replace widely used linguistic similarity features, being more than one order of magnitude faster to compute and providing greater predictive power. We test the power of such features in predicting the best answer to a question on the largest dataset from Yahoo Answers used for this task so far (40M answers) and provide a faceted analysis of the results along different topical areas and question types. We gather under a learning to rank framework the most extensive feature set used in literature to date, including 225 features from five different families. We approach the task from an application-oriented perspective, probing the space of several dimensions relevant to this problem: features, algorithms, and topics. The factors influencing the accuracy of question-answer matching are many and hard to disentangle.

Although the task of matching questions to their best answers has been tackled for more than a decade, the social question-answering practice is a complex process. Community question answering (CQA) sites use a collaborative paradigm to satisfy complex information needs.

Answering doctrinal historical and social questions