ELMo (Embeddings from Language Models) is a deep contextualized word representation model introduced by Peters et al. in 2018. ELMo is different from other word embedding models like Word2Vec and GloVe, which produce fixed embeddings for each word. Instead, ELMo produces contextualized embeddings that vary depending on the meaning of the word in a given sentence.
ELMo is based on a bidirectional LSTM (Long Short-Term Memory) neural network architecture that is trained on a large corpus of text data. The model takes as input a sequence of words and outputs a set of embeddings, one for each word in the sequence. However, unlike other models, ELMo takes into account the context in which the words appear, and the embeddings it produces are a function of the entire sentence, not just the individual words.
The name ELMo comes from the fact that the model uses a combination of different layers in the LSTM to produce the final embeddings. Each layer in the LSTM captures different aspects of the sentence, such as syntax, semantics, and pragmatics, and the final embeddings are a weighted sum of the outputs of all the layers. This allows ELMo to capture a wide range of linguistic nuances that are important for downstream natural language processing tasks.
ELMo has been shown to outperform other state-of-the-art word embedding models on a variety of natural language processing tasks, including sentiment analysis, named entity recognition, and question answering. It has also been used as a pre-trained model for transfer learning, where it is fine-tuned on a smaller dataset for a specific task.
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