(2018a) decomposes the Parsing Time Normalizations task into two subtasks: a) time entity identification using a character-level sequence tagger which detects the spans of characters that belong to each time expression and labels them with their corresponding time entity; and b) time entity composition using a simple set of rules that links relevant entities together while respecting the entity type constraints imposed by the SCATE schema.
pre-trained language models (LMs) such as ELMo (peters et al., 2018), ULMFiT (Howard and Ruder, 2018), OpenAI GpT (Radford et al., 2018), Flair (Akbik et al., 2018) and Bert (Devlin et al., 2018) have shown great improvements in NLp tasks ranging from sentiment analysis to named entity recognition to question answering.