Word-level Lexical Normalisation using Context-Dependent Embeddings
Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques.
['[EMPTY]', '[BOLD] One-hot', '[BOLD] Cumul.', '[BOLD] TF-IDF', 'Best']
In the lower half of the table, we use principle component analysis (PCA) to reduce the length of each embedding vector to 512.