Part-of-Speech Tagging › Summary

Part-of-speech tagging assigns a grammatical category label to each token in a sequence. The key ideas from this chapter:

• Tag sets define the label space: The Penn Treebank set (36 tags) is the standard for English; Universal Dependencies (17 tags) enables cross-linguistic work. The choice involves a tradeoff between granularity and generality.
• Ambiguity is the core challenge: Common words can appear as multiple parts of speech. Resolving this ambiguity requires contextual information, not just word identity. Roughly 40% of English word types are ambiguous, and those ambiguous words account for a disproportionate share of tokens.
• Features drive accuracy: Word identity, morphological suffixes, capitalization, neighboring words, and previous predicted tags are the key features. Unknown words require morphological generalization; known words rely heavily on word identity.
• Sequence modeling beats token-by-token classification: HMMs, CRFs, and neural sequence models exploit tag-to-tag dependencies that independent classifiers ignore. The globally optimal sequence found by Viterbi decoding is typically better than greedy left-to-right decoding.
• Accuracy benchmarks have a ceiling: State-of-the-art systems reach 98-99% on standard benchmarks, but the remaining 1-2% includes ambiguous tokens and annotation noise. Inter-annotator agreement itself is only 97-98%, setting a practical upper bound.
• POS tags serve downstream tasks: Named entity recognition, syntactic parsing, information extraction, chunking, and lemmatization all benefit from POS annotations. Errors in POS tagging propagate through downstream components.
• Domain matters: Taggers trained on news text degrade on social media, biomedical, or legal text. Evaluation should always include domain-matched test data.
• Cross-lingual transfer is possible: Multilingual pre-trained models enable POS tagging for low-resource languages by transferring representations from high-resource languages through a shared label space.

In the next chapter, we will see how POS tagging extends to the more complex problem of Named Entity Recognition, where the labels capture grammatical role and semantic entity type.