Sequence Tagging
Sequence tagging is a machine learning task in which an AI model assigns labels to individual elements within sequential data. Common examples include labeling words in a sentence for part-of-speech tagging, identifying named entities in text, or marking phonemes in speech. Unlike document-level classification, which assigns a single label to an entire sequence, sequence tagging produces a label for each element, creating a parallel sequence of predictions.
Technical Approach
Sequence tagging models typically process input data sequentially while considering contextual information from surrounding elements. This context dependency is important for accurate predictions, as the appropriate label for an element often depends on its neighbors. The model architecture may be recurrent neural networks, transformer-based models, or conditional random fields, with more recent approaches using transformer attention mechanisms to capture long-range dependencies efficiently.
Common Applications
Sequence tagging has practical applications in natural language processing, including named entity recognition (identifying people, organizations, and locations), part-of-speech tagging, chunking, and semantic role labeling. Beyond text, the approach applies to other sequential domains such as bioinformatics for gene annotation, time series analysis for event detection, and gesture recognition in computer vision.