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Chemical information extraction is to convert chemical knowledge in text intotrue chemical database, which is a text processing task heavily relying onchemical compound name identification and standardization. Once a systematicname for a chemical compound is given, it will naturally and much simplyconvert the name into the eventually required molecular formula. However, formany chemical substances, they have been shown in many other names besidestheir systematic names which poses a great challenge for this task. In thispaper, we propose a framework to do the auto standardization from thenon-systematic names to the corresponding systematic names by using thespelling error correction, byte pair encoding tokenization and neural sequenceto sequence model. Our framework is trained end to end and is fullydata-driven. Our standardization accuracy on the test dataset achieves 54.04%which has a great improvement compared to previous state-of-the-art result.

Chemical Names Standardization using Neural Sequence to Sequence Model