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We present an approach to textual entailment recognition , in which inference is based on a shallow semantic representation of relations (predicates and their arguments) in the text and hypothesis of the entailment pair, and in which specialized knowledge is encapsulated in modular components with very simple interfaces. We propose an architecture designed(More)
Academic collaboration has often been at the forefront of scientific progress, whether amongst prominent established researchers, or between students and advisors. We suggest a theory of the different types of academic collaboration , and use topic models to computa-tionally identify these in Computational Linguistics literature. A set of author-specific(More)
This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the query Syntactic Parsing. Our contribution in this paper is(More)
In this paper, we propose a multiword-enhanced author topic model that clusters authors with similar interests and expertise, and apply it to an information retrieval system that returns a ranked list of authors related to a keyword. For example, we can retrieve Eugene Charniak via search for statistical parsing. The existing works on author topic model-ing(More)
Academic collaboration has often been at the forefront of scientific progress, whether amongst prominent established researchers, or between students and advisors. We suggest a theory of the different types of academic collaboration , and use topic models to computa-tionally identify these in Computational Linguistics literature. A set of author-specific(More)
Organization of an email inbox can often become tedious, especially when one receives numerous emails per day. We propose an automatic label suggestion system for email, which uses an unsupervised approach to cluster related emails together based on both latent features, such as the semantics of the email, as well as direct features like the sender and(More)
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