Robert E. Mercer

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Forward Checking is a highly regarded search method used to solve Constraint Satisfaction Problems. This method performs a limited type of lookahead attempting to nd a failure earlier during a backtracking search. In this paper a new search method, Minimal Forward Checking, is introduced which under certain conditions performs the same amount of constraint(More)
Literature indexing tools provide researchers with a means to navigate through the network of scholarly scientific articles in a subject domain. We propose that more effective indexing tools may be designed using the links between articles provided by citations. With the explosion in the amount of scientific literature and with the advent of artifacts(More)
Due to the rapidly increasing amount of biomedical literature, automatic processing of biomedical papers is extremely important. Named Entity Recognition (NER) in this type of writing has several difficulties. In this paper we present a system to find phenotype names in biomedical literature. The system is based on Metamap and makes use of the UMLS(More)
The task rehearsal method (TRM) is introduced as an approach to life-long learning that uses the representation of previously learned tasks as a source of inductive bias. This inductive bias enables TRM to generate more accurate hypotheses for new tasks that have small sets of training examples. TRM has a knowledge retention phase during which the neural(More)
Scientific citations establish an explicit network of relationships among mutually relevant articles within a research field. By convention, authors include citations in their papers to indicate works that are foundational in their field, background for their own work, or representative of complementary or contradictory research. But, determining a(More)
Supervised machine learning methods for classifying spam emails are long-established. Most of these methods use either header-based or content-based features. Spammers, however, can bypass these methods easily-especially the ones that deal with header features. In this paper, we report a novel spam classification method that uses features based on email(More)