Machine translation of chemical nomenclature has considerable application?prospect in chemical text data processing between languages. However, rule based machine translation tools have to face significant complication in rule sets building, especially in translation of chemical names between English and Chinese, which are the two most used languages?of chemical nomenclature in the world. We applied two types of neural networks in the task of chemical nomenclature translation between English and Chinese, and made a comparison?with an existing rule based machine translation tool. The result shows that deep learning based approaches have a great chance to precede rule based translation tools in machine translation of chemical nomenclature between English and Chinese.
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