Choong-Nyoung Seon

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Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and Question-Answering system. This paper proposes a hybrid method of the named entity recognition which combines maximum entropy model, neural network, and pattern-selection rules. The maximum entropy model is used for the(More)
We propose an information extraction system that is designed for mobile devices with low hardware resources. The proposed system extracts temporal instances (dates and times) and named instances (locations and topics) from Korean short messages in an appointment management domain. To efficiently extract temporal instances with limited numbers of surface(More)
To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model(More)
A dialog system is an intelligent program that helps users easily access information stored in a knowledge base by formulating requests in their natural language. A dialog system needs an intention prediction module for use as a preprocessor to reduce the search space of an automatic speech recognizer. To satisfy these needs, we propose a statistical model(More)
Speaker's intention prediction modules can be widely used as a pre-processor for reducing the search space of an automatic speech re-cognizer. They also can be used as a pre-processor for generating a proper sentence in a dialogue system. We propose a statistical model to predict speakers' intentions by using multi-level features. Using the multi-level(More)