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This paper is a technical report on our system submitted to the chemicalidentification task of the BioCreative VII Track 2 challenge. The main featureof this challenge is that the data consists of full-text articles, whilecurrent datasets usually consist of only titles and abstracts. To effectivelyaddress the problem, we aim to improve tagging consistency and entity coverageusing various methods such as majority voting within the same articles fornamed entity recognition (NER) and a hybrid approach that combines a dictionaryand a neural model for normalization. In the experiments on the NLM-Chemdataset, we show that our methods improve models' performance, particularly interms of recall. Finally, in the official evaluation of the challenge, oursystem was ranked 1st in NER by significantly outperforming the baseline modeland more than 80 submissions from 16 teams.

Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles