Thomas Lin
University of Washington
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Publication
Featured researches published by Thomas Lin.
international world wide web conferences | 2012
Thomas Lin; Patrick Pantel; Michael Gamon; Anitha Kannan; Ariel Fuxman
We introduce an entity-centric search experience, called Active Objects, in which entity-bearing queries are paired with actions that can be performed on the entities. For example, given a query for a specific flashlight, we aim to present actions such as reading reviews, watching demo videos, and finding the best price online. In an annotation study conducted over a random sample of user query sessions, we found that a large proportion of queries in query logs involve actions on entities, calling for an automatic approach to identifying relevant actions for entity-bearing queries. In this paper, we pose the problem of finding actions that can be performed on entities as the problem of probabilistic inference in a graphical model that captures how an entity bearing query is generated. We design models of increasing complexity that capture latent factors such as entity type and intended actions that determine how a user writes a query in a search box, and the URL that they click on. Given a large collection of real-world queries and clicks from a commercial search engine, the models are learned efficiently through maximum likelihood estimation using an EM algorithm. Given a new query, probabilistic inference enables recommendation of a set of pertinent actions and hosts. We propose an evaluation methodology for measuring the relevance of our recommended actions, and show empirical evidence of the quality and the diversity of the discovered actions.
conference on information and knowledge management | 2009
Thomas Lin; Oren Etzioni; James Fogarty
How can we cull the facts we need from the overwhelming mass of information and misinformation that is the Web? The TextRunner extraction engine represents one approach, in which people pose keyword queries or simple questions and TextRunner returns concise answers based on tuples extracted from Web text. Unfortunately, the results returned by engines such as TextRunner include both informative facts (e.g., the FDA banned ephedra) and less useful statements (e.g., the FDA banned products). This paper therefore investigates filtering TextRunner results to enable people to better focus on interesting assertions. We first develop three distinct models of what assertions are likely to be interesting in response to a query. We then fully operationalize each of these models as a filter over TextRunner results. Finally, we develop a more sophisticated filter that combines the different models using relevance feedback. In a study of human ratings of the interestingness of TextRunner assertions, we show that our approach substantially enhances the quality of TextRunner results. Our best filter raises the fraction of interesting results in the top thirty from 41.6% to 64.1%.
north american chapter of the association for computational linguistics | 2012
Thomas Lin; Oren Etzioni
empirical methods in natural language processing | 2012
Thomas Lin; Oren Etzioni
meeting of the association for computational linguistics | 2012
Patrick Pantel; Thomas Lin; Michael Gamon
empirical methods in natural language processing | 2010
Thomas Lin; Oren Etzioni
north american chapter of the association for computational linguistics | 2010
Hoifung Poon; Janara Christensen; Pedro M. Domingos; Oren Etzioni; Raphael Hoffmann; Chloé Kiddon; Thomas Lin; Xiao Ling; Alan Ritter; Stefan Schoenmackers; Stephen Soderland; Daniel S. Weld; Fei Wu; Congle Zhang
web search and data mining | 2012
Jeff Huang; Thomas Lin; Ryen W. White
national conference on artificial intelligence | 2010
Thomas Lin; Oren Etzioni
empirical methods in natural language processing | 2012
Thomas Lin; Oren Etzioni