Information extraction as a basis for high-precision text classification
ACM Transactions on Information Systems (TOIS)
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Text categorization by boosting automatically extracted concepts
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Expert Systems with Applications: An International Journal
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We introduce the application of text categorization techniques to the astronomy field to work out semantic ambiguities between table column's names. In the astronomy field, astronomers often assign different names to table columns at their will even if they are about the same attributes of sky objects. As a result, it produces a big problem for data analysis over different tables. To solve this problem, the standard vocabulary called “unified concept descriptors (UCD)” has been defined. The reported data about sky objects can be easily analyzed through assigning columns to the predefined UCDs. In this paper, the widely used Rocchio categorization algorithm is implemented to assign UCD. An algorithm is realized to extract domain-specific semantics for text indexing while the traditional cosine-based category score model is extended by combining domain knowledge. The experiments show that Rocchio algorithm together with the proposed category score model performs well.