Enriching Word Vectors with Subword Information

Understand

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks.

• Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word.
• This is a limitation, especially for languages with large vocabularies and many rare words.
• In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character $n$-grams.