Sequence Tagging
Sequence tagging is a machine learning task in which an AI model assigns labels to individual elements within sequential data. Unlike document-level classification, which assigns a single label to an entire input, sequence tagging produces a label for each element in a sequence, creating a parallel sequence of predictions. This approach is particularly suited to problems where meaningful information is distributed across different positions in the input.
Common Applications
Sequence tagging is widely used in natural language processing tasks. Named entity recognition identifies specific entities such as persons, organizations, and locations within text. Part-of-speech tagging assigns grammatical categories to words, while chunking groups words into syntactic phrases. These techniques form the foundation for more complex language understanding systems.
Model Architectures
Modern sequence tagging models often utilize recurrent neural networks, such as Long Short-Term Memory (LSTM) networks, or transformer-based architectures like BERT. These models capture contextual dependencies between elements, allowing the label of one token to influence the prediction of adjacent tokens. The output is typically a probability distribution over possible labels for each position in the sequence, with the most likely label selected as the final prediction.