Handling Out-of-Vocabulary Words in Natural Language Processing based on Context

These word vectors are analogous to the meaning of the word. A limitation of word embeddings are that, they are learned by the Natural Language Model (word2vec, GloVe and the like) and therefore words must have been seen in the training data before, in order to have an embedding. This articles provides an approach that can be used to handle out-of-vocabulary(OOV) words in natural language processing. Given an OOV word and the sentence it is in, language modelling is used to sequence words in the sentence and predict the meaning of the word by comparison with similar sentences. This is an elegant way of learning word meanings on the fly.