Sequence Labeling

Sequence labeling is a type of machine learning task where each element in a sequence is assigned a label from a predefined set. This is commonly used in natural language processing (NLP) for tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. The main characteristics of sequence labeling include the sequential nature of the input data and the need for context to determine the appropriate label for each element. Common use cases include processing text data for applications like chatbots, information extraction, and language translation. Sequence labeling models often utilize techniques such as recurrent neural networks (RNNs) or transformers to capture dependencies between the elements in the sequence.