Snowball: extracting relations from large plain-text collections
- Eugene Agichtein, L. Gravano
- Computer ScienceDigital library
- 1 June 2000
This paper develops a scalable evaluation methodology and metrics for the task, and presents a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.
Finding high-quality content in social media
- Eugene Agichtein, C. Castillo, D. Donato, A. Gionis, G. Mishne
- Computer ScienceWeb Search and Data Mining
- 11 February 2008
This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.
A word at a time: computing word relatedness using temporal semantic analysis
- Kira Radinsky, Eugene Agichtein, E. Gabrilovich, Shaul Markovitch
- Computer ScienceThe Web Conference
- 28 March 2011
This paper proposes a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information in word semantics as a vector of concepts over a corpus of temporally-ordered documents.
Discovering authorities in question answer communities by using link analysis
- Pawel Jurczyk, Eugene Agichtein
- Computer ScienceInternational Conference on Information and…
- 6 November 2007
An analysis of the link structure of a general-purpose question answering community to discover authoritative users is presented, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site are described.
Improving Web Search Ranking by Incorporating User Behavior Information
- Eugene Agichtein, E. Brill, S. Dumais
- Computer ScienceSIGF
- 17 January 2019
It is shown that incorporating user behavior data can significantly improve ordering of top results in real web search setting, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
Learning user interaction models for predicting web search result preferences
- Eugene Agichtein, E. Brill, S. Dumais, R. Ragno
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 6 August 2006
This work presents a real-world study of modeling the behavior of web search users to predict web search result preferences and generalizes the approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone.
Mining reference tables for automatic text segmentation
- Eugene Agichtein, Venkatesh Ganti
- Computer ScienceKnowledge Discovery and Data Mining
- 22 August 2004
This paper mine tables present in data warehouses and relational databases to develop an automatic segmentation system that overcome limitations of existing supervised text segmentation approaches, and is robust, accurate, and efficient.
Predicting information seeker satisfaction in community question answering
- Yandong Liu, Jiang Bian, Eugene Agichtein
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 20 July 2008
This paper attempts to predict whether a question author will be satisfied with the answers submitted by the community participants, and presents a general prediction model, and develops a variety of content, structure, and community-focused features for this task.
Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior
- Qi Guo, Eugene Agichtein
- Computer ScienceThe Web Conference
- 16 April 2012
The experimental results show that PCB is significantly more effective than using page dwell time information alone, both for estimating the explicit judgments of each user, and for re-ranking the results using the estimated relevance.
Improving web search ranking by incorporating user behavior information
- Eugene Agichtein, E. Brill, S. Dumais
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 1 August 2006
It is shown that incorporating user behavior data can significantly improve ordering of top results in real web search setting, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
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