PartofSpeechTags

PartofSpeechTags, commonly referred to as POS tags, are labels assigned to words to indicate their syntactic category within a sentence. POS tagging is the process of assigning these tags to tokens in running text, producing a sequence of word-tag pairs. The tags help computational systems understand sentence structure beyond the surface form of words.

Tag sets vary by language and application. The Penn Treebank tagset for English includes tags such as NN, NNS, VB, VBD, JJ, RB, IN, DT, PRP. The Universal Dependencies tagset uses more general categories such as NOUN, VERB, ADJ, ADV, PROPN, DET, PRON, NUM, ADP, AUX, among others.

POS tags are produced by taggers that can be rule-based, statistical, or neural. Early approaches used hidden Markov models and conditional random fields; modern systems commonly rely on neural networks, such as BiLSTM or transformer-based models, often in a sequence-labeling setup.

POS tagging is a foundational step in many NLP tasks, including syntactic parsing, lemmatization, named-entity recognition, information extraction, machine translation, and speech recognition. It also aids search and linguistic analysis.

Challenges include lexical ambiguity, where a word assumes different tags depending on context, language-specific morphology, and tagging accuracy for noisy or informal text. Evaluation typically reports tagging accuracy against annotated corpora.