Towards robust word embeddings for noisy texts
Table 2: Accuracy results of outlier detection on 8-8-8 and wiki-sem-500 (wiki) datasets.
['[EMPTY]', 'standard 8 - 8 - 8', 'standard wiki', 'noisy 8 - 8 - 8', 'noisy wiki']
On the 8-8-8 dataset, our model outperforms the baselines both in the standard and noisy scenarios, although with visibly lower margins than in the case of semantic similarity. However, on the wiki-sem-500 dataset, word2vec outperforms its competitors on standard words and does not lose much performance on the noisy setup. The latter may be explained by the low amount of successfully denormalized words, with just 7.5% of the total (compared to 52.2% on the 8-8-8 dataset), which also hints to the tie between fastText and our model.