Eugene Agichtein
Personal Homepage:
mathcs.emory.edu/~eugene/Current place of employment:
Emory UniversityEugene Agichtein is an Assistant Professor in the Mathematics and Computer Science Department at Emory University. Previously, Eugene was a Postdoctoral Researcher in the Text Mining, Search, and Navigation group at Microsoft Research, working on text and web mining for information retrieval. He received a Ph.D. in Computer Science from Columbia University in 2005, and a B.S. in Engineering from The Cooper Union in 1998. Eugene is a recipient of the “Best Student Paper” award at the ICDE 2003 conference, and the “Best Paper Award” at SIGMOD 2006 conference. Dr. Agichtein's research interests are in discovering and managing information in large unstructured datasets, with emphasis on the web and life sciences domains.
Publications by Eugene Agichtein (bibliography)
» 2009 «
Bian, Jiang, Liu, Yandong, Zhou, Ding, Agichtein, Eugene and Zha, Hongyuan (2009): Learning to recognize reliable users and content in social media with coupled mutual reinforcement. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 51-60. Available online
“Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely -- increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.”
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» 2008 «
Liu, Yandong, Bian, Jiang and Agichtein, Eugene (2008): Predicting information seeker satisfaction in community question answering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 483-490. Available online
“Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours -- and sometimes days -- until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing.”
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Guo, Qi and Agichtein, Eugene (2008): Exploring mouse movements for inferring query intent. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 707-708. Available online
“Clickthrough on search results have been successfully used to infer user interest and preferences, but are often noisy and potentially ambiguous. We explore the potential of a complementary, more sensitive signal -- mouse movements -- in providing insights into the intent behind a web search query. We report preliminary results of studying user mouse movements on search result pages, with the goal of inferring user intent -- in particular, to explore whether we can automatically distinguish the different query classes such as navigational vs. informational. Our preliminary exploration confirms the value of studying mouse movements for user intent inference, and suggests interesting avenues for future exploration.”
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Li, Baoli, Liu, Yandong, Ram, Ashwin, Garcia, Ernest V. and Agichtein, Eugene (2008): Exploring question subjectivity prediction in community QA. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 735-736. Available online
“In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.”
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Liu, Yandong and Agichtein, Eugene (2008): On the evolution of the yahoo! answers QA community. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 737-738. Available online
“While question answering communities have been gaining popularity for several years, we wonder if the increased popularity actually improves or degrades the user experience. In addition, automatic QA systems, which utilize different sources such as search engines and social media, are emerging rapidly. QA communities have already created abundant resources of millions of questions and hundreds of millions of answers. The question whether they will continue to serve as an effective source is of information for web search and question answering is of vital importance. In this poster, we investigate the temporal evolution of a popular QA community -- Yahoo! Answers, with respect to its effectiveness in answering three basic types of questions: factoid, opinion and complex questions. Our experiments show that Yahoo! Answers keeps growing rapidly, while its overall quality as an information source for factoid question-answering degrades. However, instead of answering factoid questions, it might be more effective to answer opinion and complex questions.”
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Bian, Jiang, Liu, Yandong, Agichtein, Eugene and Zha, Hongyuan (2008): Finding the right facts in the crowd: factoid question answering over social media. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 467-476. Available online
“Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, to find out an obscure piece of trivia, it is now possible and even very effective to post a question on a popular community QA site such as Yahoo! Answers, and to rely on other users to provide answers, often within minutes. The importance of such community QA sites is magnified as they create archives of millions of questions and hundreds of millions of answers, many of which are invaluable for the information needs of other searchers. However, to make this immense body of knowledge accessible, effective answer retrieval is required. In particular, as any user can contribute an answer to a question, the majority of the content reflects personal, often unsubstantiated opinions. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the contents of community QA archives differ significantly from the web setting. To address this problem we present a general ranking framework for factual information retrieval from social media. Results of a large scale evaluation demonstrate that our method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark. We also show that our learning framework can be tuned with the minimum of manual labeling. Finally, we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better integrate social media content for effective information access.”
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» 2007 «
Clarke, Charles L. A., Agichtein, Eugene, Dumais, Susan and White, Ryen W. (2007): The influence of caption features on clickthrough patterns in web search. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 135-142. Available online
“Web search engines present lists of captions, comprising title, snippet, and URL, to help users decide which search results to visit. Understanding the influence of features of these captions on Web search behavior may help validate algorithms and guidelines for their improved generation. In this paper we develop a methodology to use clickthrough logs from a commercial search engine to study user behavior when interacting with search result captions. The findings of our study suggest that relatively simple caption features such as the presence of all terms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.”
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Jurczyk, Pawel and Agichtein, Eugene (2007): Hits on question answer portals: exploration of link analysis for author ranking. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 845-846. Available online
“Question-Answer portals such as Naver and Yahoo! Answers are growing in popularity. However, despite the increased popularity, the quality of answers is uneven, and while some users usually provide good answers, many others often provide bad answers. Hence, estimating the authority, or the expected quality of users, is a crucial task for this emerging domain, with potential applications to answer ranking and to incentive mechanism design. We adapt a powerful link analysis methodology from the web domain as a first step towards estimating authority in Question Answer portals. Our experimental results over more than 3 million answers from Yahoo! Answers are promising, and warrant further exploration along the lines outlined in this poster.”
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Jurczyk, Pawel and Agichtein, Eugene (2007): Discovering authorities in question answer communities by using link analysis. In: Silva, Mario J., Laender, Alberto H. F., Baeza-Yates, Ricardo A., McGuinness, Deborah L., Olstad, Bjørn, Olsen, Øystein Haug and Falcão, André O. (eds.) Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management - CIKM 2007 November 6-10, 2007, Lisbon, Portugal. pp. 919-922. Available online
Agichtein, Eugene, Burges, Chris and Brill, Eric (2007): Question Answering over Implicitly Structured Web Content. In: 2007 IEEE / WIC / ACM International Conference on Web Intelligence WI 2007 2-5 November, 2007, Silicon Valley, CA, USA. pp. 18-25. Available online
» 2006 «
Agichtein, Eugene, Brill, Eric, Dumais, Susan and Ragno, Robert (2006): Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 3-10. Available online
“Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate modeling and interpretation of user behavior has important applications to ranking, click spam detection, web search personalization, and other tasks. Our key insight to improving robustness of interpreting implicit feedback is to model query-dependent deviations from the expected "noisy" user behavior. We show that our model of clickthrough interpretation improves prediction accuracy over state-of-the-art clickthrough methods. We generalize our approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone. We report results of a large-scale experimental evaluation that show substantial improvements over published implicit feedback interpretation methods.”
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Agichtein, Eugene, Brill, Eric and Dumais, Susan (2006): Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 19-26. Available online
“We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.”
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» 2005 «
Agichtein, Eugene, Cucerzan, Silviu and Brill, Eric (2005): Analysis of factoid questions for effective relation extraction. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 567-568. Available online
“We present an analysis of the structured relationships observed in a randomly sampled set of question-like queries submitted to a search engine for a popular online encyclopedic document collection. Our study shows that a relatively small number of binary relationships account for most of the queries in the sample. This empirically validates an approach of analyzing query logs to identify the relationships most relevant to user needs and populating corresponding fact tables from the collection for factoid question answering. Our analysis shows that such an approach can lead to substantial coverage of user questions.”
Copyrights may apply
Agichtein, Eugene and Cucerzan, Silviu (2005): Predicting accuracy of extracting information from unstructured text collections. In: Herzog, Otthein, Schek, Hans-Jörg and Fuhr, Norbert (eds.) Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management October 31 - November 5, 2005, Bremen, Germany. pp. 413-420. Available online
» 2004 «
Agichtein, Eugene, Lawrence, Steve and Gravano, Luis (2004): Learning to find answers to questions on the Web. In ACM Trans. Internet Techn., 4 (2) pp. 129-162
» 2001 «
Agichtein, Eugene, Lawrence, Steve and Gravano, Luis (2001): Learning search engine specific query transformations for question answering. In: Proceedings of the 2001 International Conference on the World Wide Web 2001. pp. 169-178. Available online
» 2000 «
Agichtein, Eugene and Gravano, Luis (2000): Snowball: Extracting Relations from Large Plain-Text Collections. In: DL00: Proceedings of the 5th ACM International Conference on Digital Libraries 2000. pp. 85-94. Available online
“Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or running data mining tasks. We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns, that in turn result in new tuples being extracted from the document collection. We build on this idea and present our Snowball system. Snowball introduces novel strategies for generating patterns and extracting tuples from plain-text documents. At each iteration of the extraction process, Snowball evaluates the quality of these patterns and tuples without human intervention, and keeps only the most reliable ones for the next iteration. In this paper we also develop a scalable evaluation methodology and metrics for our task, and present a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.”
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Mar 21st, 2010
Changes to this page (author)
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