This paper proposes an integrated method for extracting the time information of events from Japanese news archives. We first utilize a new pattern-based method named TRE/ERT combined with a neural-based model to extract all temporal expressions possibly related with an event. Then, we apply a simple but efficient clustering and narrowing process to summarize these temporal expressions into a small time frame for events lasting shorter than a day, or time frames for the beginning and the end days of the events for events spanning multiple days. We conducted two experiments where the results show that when working with one-day events, our system has a precision high up to 57% and the rate that the actual date of the event falls in our extracted time frame reaches 100% as long as the event name is found in the archive. The results also show that our system works with multiple-day events, but needs further improvements to get better results.

In this paper, we explore and discuss a way to extract temporal information from natural language texts. The suggested method is divided into two parts: temporal expression recognition and temporal value inference. The former employs the conventional NER approach, using a BiLSTM-CRF architecture. The latter is implemented with a rule-based algorithm, which can be further developed in later work for better coverage of various temporal expressions. In terms of the corpus, we have selected 200 articles from one of the major Japanese newspaper companies to create an annotated corpus, classifying temporal expressions into five different types. As for the performance, we have achieved 0.866 in F-measure for the recognition of temporal expressions and 0.920 in accuracy for the inference of the absolute temporal values of the expressions. Combining the two modules and running them as an end-to-end system, we have attained 0.891 of F-measure.