In this theoretical paper, we compare the "classical" learning techniques used to infer regular grammars from positive examples with the ones used to infer categorial gra...
We present an algorithm, Nomen, for learning generalized names in text. Examples of these are names of diseases and infectious agents, such as bacteria and viruses. These names ex...
We cast the problem of recognizing related categories as a unified learning and structured prediction problem with shared body plans. When provided with detailed annotations of o...
Ian Endres, Vivek Srikumar, Ming-Wei Chang, Derek ...
With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. Recently...
Ralph Gross, Latanya Sweeney, Fernando De la Torre...
We present a method for extracting selectional preferences of verbs from unannotated text. These selectional preferences are linked to an ontology (e.g. the hypernym relations foun...