Approaches to passage retrieval in full text information systems
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Subtopic structuring for full-length document access
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Passage-level evidence in document retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient retrieval of partial documents
TREC-2 Proceedings of the second conference on Text retrieval conference
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Efficient passage ranking for document databases
ACM Transactions on Information Systems (TOIS)
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Effective ranking with arbitrary passages
Journal of the American Society for Information Science and Technology
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A multi-level matching method with hybrid similarity for document retrieval
Expert Systems with Applications: An International Journal
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Reduction of training noises for text classifiers
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Researches in text categorization have been confined to whole-document-level classification, probably due to lack of full-text test collections. However, full-length documents available today in large quantities pose renewed interests in text classification. A document is usually written in an organized structure to present its main topic(s). This structure can be expressed as a sequence of subtopic text blocks, or passages. In order to reflect the subtopic structure of a document, we propose a new passage-level or passage-based text categorization model, which segments a test document into several passages, assigns categories to each passage, and merges the passage categories to the document categories. Compared with traditional document-level categorization, two additional steps, passage splitting and category merging, are required in this model. Using four subsets of the Reuters text categorization test collection and a full-text test collection of which documents are varying from tens of kilobytes to hundreds, we evaluate the proposed model, especially the effectiveness of various passage types and the importance of passage location in category merging. Our results show simple windows are best for all test collections tested in these experiments. We also found that passages have different degrees of contribution to the main topic(s), depending on their location in the test document.