2024 elmo first appearance One of the key innovations of ELMo is its use of character-level convolutional neural networks (CNNs) to generate embeddings for words. This allows ELMo to handle out-of-vocabulary words, or words that were not seen during training, by breaking them down into their constituent characters and generating embeddings for each character. These character-level embeddings are then combined to form a single embedding for the word.
ELMo is a type of language model that generates embeddings, or dense vector representations, for words based on their context in a sentence. Unlike traditional word embeddings, such as Word2Vec or GloVe, which generate a single vector representation for each word that is fixed across all contexts, ELMo generates a unique vector representation for each word in each sentence, allowing it to capture the nuanced meanings of words that can change based on context. The ELMo model is a two-layer LSTM (Long Short-Term Memory) network that is trained on a large corpus of text data to predict the next word in a sentence. The first layer of the network generates a set of embeddings for each word based on its local context, which includes the words immediately before and after it in the sentence. The second layer of the network then refines these embeddings based on the broader context of the sentence, allowing it to capture more complex linguistic phenomena such as syntax and semantics. One of the key innovations of ELMo is its use of character-level convolutional neural networks (CNNs) to generate embeddings for words. This allows ELMo to handle out-of-vocabulary words, or words that were not seen during training, by breaking them down into their constituent characters and generating embeddings for each character. These character-level embeddings are then combined to form a single embedding for the word.
Copyright 2024 All Right Reserved By.