Sequence Labeling
Sequence labeling is a fundamental task in natural language processing (NLP) and machine learning that involves assigning a label to each element in a sequence of data, such as words in a sentence or nucleotides in a DNA strand. It is commonly used for tasks like named entity recognition (NER), part-of-speech tagging, and chunking, where the goal is to predict a sequence of labels that correspond to the input sequence. This technique leverages models that consider the context of each element to make accurate predictions, often using algorithms like Hidden Markov Models (HMMs), Conditional Random Fields (CRFs), or neural networks like LSTMs and Transformers.
Developers should learn sequence labeling when working on NLP applications that require structured output from sequential data, such as extracting entities from text for information retrieval, tagging parts of speech for syntactic analysis, or segmenting biological sequences in bioinformatics. It is essential for building systems that need to understand and annotate sequences in domains like chatbots, search engines, and medical data processing, where context-aware predictions improve accuracy and functionality.