Contextualized word distributed representation for Chemical NER

Abstract

To improve efficiency in discovering new compounds and searching path synthetic pathway with the machine learning has received attention and there is big demand to extract chemical information from document data automatically. The named entity recognition that is a task to detect chemical entity from documents is a fundamental and important process for chemical information extraction. In the named entity recognition, the BiLSTM-CRF model has been widely used. The input of model is a sequence of words. The words are converted to vector that is called the distributed representation. Recently, it has been reported that the contextualized distributed representation improves the performance of the neural model for the named entity recognition. In this paper, to apply these approaches to chemical informatics domain, we employ a contextualized word representation combined to the BiLSTM-CRF and our method achieved the state-of-the-art performance in the chemical named entity recognition task.