Towards robust word embeddings for noisy texts
Table 4: Accuracy results of the extrinsic evaluation on SemEval (SE) Twitter SA datasets.
['[EMPTY]', 'SE13 B', 'SE14 B', 'SE16 BD', 'SE16 CE']
In this case, word2vec continues to display a strong performance, fastText loses the advantage it had on the SentEval benchmark for the same SA task, and our approach is able to revert this performance loss to outperform, once again, both of the baselines. At this point, we can observe how fastText is inferior to word2vec on a real-world social media setting, when we may have expected the opposite at first. But for this same reason, it is remarkable to see our approach taking the lead despite being a modification of fastText, which also demonstrates the benefit of including the bridge-words at training time. Having said that, it would be relevant to investigate if higher performance figures can be obtained by modifying the skipgram model from word2vec.