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
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Comparing Temporal Behavior of Phrases on Multiple Indexes with a Burst Word Detection Method
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Discovering research key terms as temporal patterns of importance indices for text mining
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Evaluating a temporal pattern detection method for finding research keys in bibliographical data
Transactions on rough sets XIV
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In this paper, we propose a method for detecting temporal trends of technical terms based on importance indices and linear regression methods. In text mining, importance indices of terms such as simple frequency, document frequency including the terms, and tf-idf of the terms, play a key role for finding valuable patterns in documents. As for the documents, they are often published daily, monthly, annually, and irregularly for each purpose. Although the purposes of each set of documents are not changed, roles of terms and the relationship among them in the documents change temporally. In order to detect such temporal changes, we combined a method to extract terms, importance indices of terms, and trend identification based on linear regression analysis. Empirical results show that our method detected emergent and subsiding trends of extracted terms in a corpus of a research domain. By comparing this method with the existing burst detection method, we investigated the trend of phrases consisting of several burst words in the titles of AAAI and IJCAI.