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

No abstract

No abstract

Keywords: Phosphane selenide dibromides / Heteronuclear NMR / Raman spectroscopy / X-ray structures / Bromine exchange / Ab initio calculations

The structures of the first phosphane telluride complexes of silver(I), obtained from i-Pr3PTe (1) with AgNMs2 [Ms = SO2CH3] and with AgSbF6, reveal the superior coordinating ability of 1, particularly as a bridging ligand, compared with related i-Pr3PS and i-Pr3PSe ligands.