  This paper is a technical report on our system submitted to the chemical
identification task of the BioCreative VII Track 2 challenge. The main feature
of this challenge is that the data consists of full-text articles, while
current datasets usually consist of only titles and abstracts. To effectively
address the problem, we aim to improve tagging consistency and entity coverage
using various methods such as majority voting within the same articles for
named entity recognition (NER) and a hybrid approach that combines a dictionary
and a neural model for normalization. In the experiments on the NLM-Chem
dataset, we show that our methods improve models' performance, particularly in
terms of recall. Finally, in the official evaluation of the challenge, our
system was ranked 1st in NER by significantly outperforming the baseline model
and more than 80 submissions from 16 teams.
