Temporal Classification of Text and Automatic Document Dating

Temporal information is presently underutilised for document and text processing purposes. This work presents an unsupervised method of extracting periodicity information from text, enabling time series creation and filtering to be used in the creation of sophisticated language models that can discern between repetitive trends and non-repetitive writing pat-terns. The algorithm performs in O(n log n) time for input of length n. The temporal language model is used to create rules based on temporal-word associations inferred from the time series. The rules are used to automatically guess at likely document creation dates, based on the assumption that natural languages have unique signatures of changing word distributions over time. Experimental results on news items spanning a nine year period show that the proposed method and algorithms are accurate in discovering periodicity patterns and in dating documents automatically solely from their content.