A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Discovery of inference rules for question-answering
Natural Language Engineering
Improving text categorization using the importance of sentences
Information Processing and Management: an International Journal
A study on automatically extracted keywords in text categorization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Using bag-of-concepts to improve the performance of support vector machines in text categorization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
An exploratory study of news article clustering for web-based bio-surveillance
Proceedings of the 1st ACM International Health Informatics Symposium
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Traditional text classification studied in the information retrieval and machine learning literature is mainly based on topics. That is, each class represents a particular topic, e.g., sports and politics. However, many real-world problems require more refined classification based on some semantic perspectives. For example, in a set of sentences about a disease, some may report outbreaks of the disease, some may describe how to cure the disease, and yet some may discuss how to prevent the disease. To classify sentences at this semantic level, the traditional bag-of-words model is no longer sufficient. In this paper, we study semantic sentence classification of disease reporting. We show that both keywords and sentence semantic features are useful. Our results demonstrated that this integrated approach is highly effective.