I'm building a sentiment analysis tool for social media data where users use a lot of slang and typos. My Word2Vec model is struggling with "Out of Vocabulary" words. Would switching to subword tokenization like Byte Pair Encoding (BPE) or WordPiece solve this? How do these methods actually represent a word the model hasn't seen before?
3 answers
Switching to subword tokenization is exactly what you need for social media data. Word2Vec is word-level, so if it hasn't seen "happpppy" with five 'p's, it just returns an "unknown" token. BPE, which is used by models like GPT, breaks words down into the smallest frequent character sequences. If it sees a new typo, it might break it into "hap," "pp," and "py." The model has seen those fragments before, so it can still construct a meaningful vector representation. This is why modern Transformers almost never have OOV issues—they can essentially "spell out" any word using their subword vocabulary.
This makes a lot of sense for typos, but does the increased number of tokens per sentence significantly slow down the training time for large datasets?
Try the fastText library if you want to stay with word embeddings. It uses character n-grams, which allows it to handle OOV words much better than the original Word2Vec implementation.
Great point, Kimberly. fastText is a fantastic "middle ground" if you aren't ready to move to a full Transformer architecture yet but need to handle messy text.
That’s a valid concern, Jeffrey. While subword tokenization does increase the total token count compared to simple whitespace splitting, it actually makes the model more efficient overall. Because the "Vocabulary Size" is fixed and much smaller (usually 30k to 50k tokens), the embedding layer is significantly leaner than a word-level model that might need millions of rows to cover all slang. The computational trade-off is almost always worth the massive jump in model robustness and generalization.