  Lexical normalisation (LN) is the process of correcting each word in a
dataset to its canonical form so that it may be more easily and more accurately
analysed. Most lexical normalisation systems operate at the character-level,
while word-level models are seldom used. Recent language models offer solutions
to the drawbacks of word-level LN models, yet, to the best of our knowledge, no
research has investigated their effectiveness on LN. In this paper we introduce
a word-level GRU-based LN model and investigate the effectiveness of recent
embedding techniques on word-level LN. Our results show that our GRU-based
word-level model produces greater results than character-level models, and
outperforms existing deep-learning based LN techniques on Twitter data. We also
find that randomly-initialised embeddings are capable of outperforming
pre-trained embedding models in certain scenarios. Finally, we release a
substantial lexical normalisation dataset to the community.
