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 and classifies proper nouns such as person names, organizations, and locations within text. Part-of-speech tagging labels each word in a sentence with its grammatical role. Chunking segments sentences into noun phrases and verb phrases. In bioinformatics, sequence tagging identifies functional regions within DNA or protein sequences.
Technical Approaches
Various architectures are used for sequence tagging, including hidden Markov models, conditional random fields, and recurrent neural networks such as LSTMs. More recent approaches employ transformer-based models that process entire sequences in parallel rather than sequentially. These models typically output a probability distribution over possible labels at each position, with the final tag selected through decoding strategies like Viterbi decoding or greedy selection.