A statistical interpretation of term specificity and its application in retrieval
Document retrieval systems
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Extracting nested collocations
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Language-Independent Set Expansion of Named Entities Using the Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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In text mining processes, temporal text mining have attracted considerable attention as an one of the important issues for finding remarkable terms with temporal patterns in temporal set of documents. Although importance indices of the technical terms play a key role in finding valuable patterns from various documents, temporal changes of them are not explicitly treated by conventional methods. Since those methods depend on particular index in each method, they are not robust in changes of terms. In order to detect remarkable temporal trends of technical terms in given textual datasets robustly, we propose a method based on temporal changes in several importance indices by assuming the importance indices of the terms to be a dataset. Our empirical study shows that two representative importance indices are applied to the documents from a research area. After detecting the temporal trends, we compared the emergent trend of the technical phrases to some emergent phrases given by a domain expert.