contextualized rep- resentations

Contextualized representations refer to a type of language representation that takes into account the context in which a word or phrase appears. This means that the meaning of a word can change depending on the context in which it is used. For example, the word "bank" can refer to a financial institution or the edge of a river, depending on the context in which it is used. Contextualized representations are used in natural language processing (NLP) tasks such as language translation, sentiment analysis, and question answering. One popular method for generating contextualized representations is through the use of neural network models such as transformer-based models like BERT and GPT.

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deep contextualized word representations

深度上下文化的单词表示是指通过深度学习算法对单词进行建模，将其嵌入到高维向量空间中，并考虑上下文信息，以便更好地理解单词的含义和用法。这种表示方法可以用于自然语言处理任务，如文本分类、命名实体识别、机器翻译等。