POSTagging

Part-of-speech tagging, or POS tagging, is the process of assigning a syntactic category to each word token in a text, such as noun, verb, adjective, or determiner. POS tags provide a shallow layer of linguistic information that supports many natural language processing tasks, including syntactic parsing, information extraction, machine translation, and search and voice-enabled interfaces.

Tagging typically follows tokenization. Approaches are rule-based, statistical, or neural. Rule-based methods rely on hand-crafted grammars and dictionaries. Statistical methods learn the most probable tag for a token given its context, using models such as hidden Markov models or conditional random fields. Neural approaches, especially bidirectional LSTM models and transformer architectures with a tagging head, produce context-sensitive tags and often employ a CRF layer to enforce valid tag sequences. Tag sets vary; for English, the Penn Treebank tag set is widely used. Training requires annotated corpora that provide word–tag pairs. For example, in the sentence “The quick brown fox jumps over the lazy dog,” a common tag sequence is The/DT quick/JJ brown/JJ fox/NN jumps/VBZ over/IN the/DT lazy/JJ dog/NN.

Evaluation and challenges: Accuracy is measured as the proportion of tokens tagged correctly on a labeled test set. On standard English benchmarks, taggers often achieve around 97–98% accuracy on the WSJ portion of the Penn Treebank. Challenges include lexical ambiguity, multiword expressions, rare or unseen words, noisy input, and domain or language-specific phenomena in non-English texts.

Applications and resources: POS tags support parsing, information extraction, morphological analysis, and downstream NLP tasks. Widely used tools include NLTK, SpaCy, the Stanford POS Tagger, and projects based on Universal Dependencies. Public corpora and pre-trained models enable tagging for many languages and domains.