partofspeechtagging

Part-of-speech tagging, often abbreviated POS tagging, is the process of assigning a grammatical category to each token in a text. Common categories include noun, verb, adjective, adverb, pronoun, preposition, conjunction, determiner, and punctuation. Tagging typically operates after tokenization and before higher-level linguistic processing such as parsing. Taggers may use different tag sets, so the exact labels can vary by language and annotation scheme.

Approaches: Rule-based taggers rely on hand-crafted dictionaries and morphologic rules to assign tags; statistical taggers learn probabilities from annotated corpora. Classic statistical models include Hidden Markov Models and Conditional Random Fields, which combine lexical observations with sequence information. More recently, neural approaches use recurrent or transformer-based architectures that learn representations and predict tags, often with sequence labeling frameworks such as a BiLSTM-CRF or a transformer with a tagging head.

Tag sets and data: Tag sets differ by language. The Penn Treebank tag set for English is widely used in NLP research, while Universal Dependencies provides a cross-linguistic set of universal POS labels. Tagged corpora provide the supervision needed to train taggers and to evaluate them. Noisy or domain-specific text may require adaptation or retraining.

Applications and evaluation: POS tagging is a foundational step in syntactic parsing, information extraction, machine translation, and speech recognition. It is typically evaluated by tagging accuracy, the proportion of tokens assigned the correct tag. High-performing English POS taggers reach accuracies above 97% on standard benchmarks; performance can vary by domain and language.

Challenges: Ambiguity and context drive many tagging errors, particularly with polysemous words, proper nouns, or words that change part of speech by sentence. Data sparsity, languages with rich morphology, multiword expressions, and code-switching pose additional difficulties. Annotation schemes differ, so cross-domain or cross-language tagging requires normalization.