Justin Betteridge
Carnegie Mellon University
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Publication
Featured researches published by Justin Betteridge.
web search and data mining | 2010
Andrew Carlson; Justin Betteridge; Richard C. Wang; Estevam R. Hruschka; Tom M. Mitchell
We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.
conference on information and knowledge management | 2012
Freddy Chong Tat Chua; William W. Cohen; Justin Betteridge; Ee-Peng Lim
Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the authors behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.
international conference on knowledge capture | 2005
Eric Nyberg; Teruko Mitamura; Justin Betteridge
This paper describes a prototype system which captures semantic knowledge from domain text using controlled language. The KANTOO system is used to analyze input sentences from college-level science textbooks, producing sentence-level meaning representations (interlingua). The interlingua expressions are mapped into F-logic statements, which are be stored in a separate knowledge base to support reasoning in the domain.
national conference on artificial intelligence | 2010
Andrew Carlson; Justin Betteridge; Bryan Kisiel; Burr Settles; Estevam R. Hruschka; Tom M. Mitchell
north american chapter of the association for computational linguistics | 2009
Andrew Carlson; Justin Betteridge; Estevam Rafael Hruschka Junior; Tom M. Mitchell
national conference on artificial intelligence | 2015
Tom M. Mitchell; William W. Cohen; E. Hruschka; Partha Pratim Talukdar; Justin Betteridge; Andrew Carlson; Bhavana Dalvi; Matt Gardner; Bryan Kisiel; Jayant Krishnamurthy; Ni Lao; Kathryn Mazaitis; T. Mohamed; Ndapandula Nakashole; Emmanouil Antonios Platanios; Alan Ritter; Mehdi Samadi; Burr Settles; Richard C. Wang; Derry Tanti Wijaya; Abhinav Gupta; Xi Chen; A. Saparov; M. Greaves; J. Welling
text retrieval conference | 2007
Nico Schlaefer; Jeongwoo Ko; Justin Betteridge; Manas A. Pathak; Eric Nyberg; Guido Sautter
international semantic web conference | 2009
Tom M. Mitchell; Justin Betteridge; Andrew Carlson; Estevam R. Hruschka; Richard C. Wang
national conference on artificial intelligence | 2009
Justin Betteridge; Andrew Carlson; Sue Ann Hong; Estevam R. Hruschka; Edith Law; Tom M. Mitchell; Sophie H. Wang
NTCIR | 2007
Teruko Mitamura; Frank Lin; Hideki Shima; Mengqiu Wang; Jeongwoo Ko; Justin Betteridge; Matthew W. Bilotti; Andrew Hazen Schlaikjer; Eric Nyberg