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Chemical patents are an important resource for chemical information. However,few chemical Named Entity Recognition (NER) systems have been evaluated onpatent documents, due in part to their structural and linguistic complexity. Inthis paper, we explore the NER performance of a BiLSTM-CRF model utilisingpre-trained word embeddings, character-level word representations andcontextualized ELMo word representations for chemical patents. We compare wordembeddings pre-trained on biomedical and chemical patent corpora. The effect oftokenizers optimized for the chemical domain on NER performance in chemicalpatents is also explored. The results on two patent corpora show thatcontextualized word representations generated from ELMo substantially improvechemical NER performance w.r.t. the current state-of-the-art. We also show thatdomain-specific resources such as word embeddings trained on chemical patentsand chemical-specific tokenizers have a positive impact on NER performance.

Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings