  Research on word embeddings has mainly focused on improving their performance
on standard corpora, disregarding the difficulties posed by noisy texts in the
form of tweets and other types of non-standard writing from social media. In
this work, we propose a simple extension to the skipgram model in which we
introduce the concept of bridge-words, which are artificial words added to the
model to strengthen the similarity between standard words and their noisy
variants. Our new embeddings outperform baseline models on noisy texts on a
wide range of evaluation tasks, both intrinsic and extrinsic, while retaining a
good performance on standard texts. To the best of our knowledge, this is the
first explicit approach at dealing with this type of noisy texts at the word
embedding level that goes beyond the support for out-of-vocabulary words.
