In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appr...
Abstract. We propose a semantic tagger that provides high level concept information for phrases in clinical documents. It delineates such information from the statements written by...
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...
We propose an HMM-based text-indicated writer verification method, which is based on a challenge and response type of authentication process. In this method, a different text incl...
Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, a...