ELMo
ELMo (Embeddings from Language Models) is a deep contextualized word representation model developed by AllenNLP that generates word embeddings based on the entire input sentence, capturing complex characteristics like syntax and semantics. It uses a bidirectional LSTM trained on a large text corpus to produce context-sensitive embeddings that vary depending on how words are used in different contexts. This approach improves performance in various natural language processing tasks by providing richer linguistic features than static word embeddings.
Developers should learn ELMo when working on NLP tasks that require understanding word meaning in context, such as sentiment analysis, named entity recognition, or question answering, as it handles polysemy and syntactic nuances effectively. It is particularly useful in research or applications where pre-trained contextual embeddings can boost model accuracy without extensive custom training, making it a foundational tool in modern NLP pipelines before the rise of transformer-based models like BERT.