Chemical Names Standardization using Neural Sequence to Sequence Model
This work addresses the challenge of chemical information extraction for researchers and databases by automating name standardization, though it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of standardizing non-systematic chemical names to systematic names using a neural sequence-to-sequence model, achieving a standardization accuracy of 54.04% on a test dataset, which represents a significant improvement over previous state-of-the-art results.
Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.