Abstract
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, character-level 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.
Highlights
Chemical patents are an important starting point for understanding of chemical compound purpose, properties, and novelty
New chemical compounds are often initially disclosed in patent documents; it may take 1-3 years for these chemicals to be mentioned in chemical literature (Senger et al, 2015), suggesting that patents are a valuable but underutilized resource
The results show that contextualized word representations help improve chemical Named-Entity Recognition (NER) performance substantially
Summary
Chemical patents are an important starting point for understanding of chemical compound purpose, properties, and novelty. Authors strive to make their words as clear and straight-forward as possible, whereas patent authors often seek to protect their knowledge from being fully disclosed (Valentinuzzi, 2017). In tension with this is the need to claim broad scope for intellectual property reasons, and patents typically contain more details and are more exhaustive than scientific papers (Lupu et al, 2011). The results show that word embeddings that are pre-trained on chemical patents outperform embeddings pre-trained on biomedical datasets, and using tokenizers optimized for the chemical domain can improve NER performance in chemical patent corpora
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