Inference of Absolute Time Value from Temporal Expressions

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.