--- title: "A Simple Rule-Based Part of Speech Tagger — Brill 1992" description: "Brill asks whether part-of-speech tagging really needs stochastic (Markov-model) methods, or whether a plain rule-based tagger with no syntactic…" authors: ["Eric Brill"] year: 1992 venue: "Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992" source: "https://aclanthology.org/H92-1022/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/brill-1992-simple-rule-based-part-speech/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/brill-1992-simple-rule-based-part-speech/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [1992](https://md.research.elamur.ai/free/en/papers/1992/) / A Simple Rule-Based Part of Speech Tagger # A Simple Rule-Based Part of Speech Tagger **Authors:** [Eric Brill](https://md.research.elamur.ai/free/en/authors/eric-brill/) · **Year:** 1992 · **Venue:** Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992 · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/H92-1022/) · [PDF](https://aclanthology.org/H92-1022.pdf) · [OpenAlex](https://openalex.org/W2081687495) **Citations:** 1413 citations (openalex-snapshot · 2026-10-14) · [205](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Brill asks whether part-of-speech tagging really needs stochastic (Markov-model) methods, or whether a plain rule-based tagger with no syntactic knowledge can match them. His tagger starts by giving every word its most frequent tag from a tagged corpus. Unseen words get a guess: capitalized unknowns become proper nouns, and other unknowns take the tag most typical for their final three letters. The tagger then learns corrective "patches" from a fixed set of templates, such as conditions on neighboring tags or capitalization. Each patch is chosen greedily for the largest net error reduction on a held-out patch corpus. The experiments use the Brown Corpus. About 71 learned rules bring the error rate down to roughly 5%. That is close to the author's own reimplementation of a stochastic tagger, and it needs no dictionary or hand-built idiom lists. The work matters because it shows a compact, readable, and portable rule set can rival large probability tables. This later became the basis of transformation-based learning. ## Key points - **Data:** Brown Corpus, about 1.1M words with 192 tags, 96 of which occur more than 100 times. It was split into 90% for training the lexical tagger, 5% as a patch corpus for rule learning, and 5% as a test set covering all genres. - **Baseline and result:** The baseline (most-frequent tag plus the unknown-word heuristics) has about 7.9% error. Applying 71 learned patches lowers this to 5.1%. Three different random splits all gave about 5%. - **Rule effects on the test set:** Of the 71 patches, 66 reduced errors, 3 had no net effect, and 2 increased errors. - **Comparison:** The author's dictionary-free reimplementation of the stochastic tagger from reference [1] reached about 4.5% error on the same splits. The rule tagger needs fewer than 80 rules, while a trigram model needs tens of thousands of statistics. - **Unknown words:** When every word is known, the same patches give 4.1% error if the lexicon comes from the whole Brown Corpus, and 3.5% if it comes from the test text itself. This suggests a dictionary would help. ## Interesting ideas - **Net-gain rule scoring:** Each candidate rule is scored by the errors it fixes minus the errors it creates. For example, one rule fixed 98 of 159 verb-for-noun errors but introduced 18 new ones, for a net gain of 80. This greedy, error-driven loop could be applied to many sequence-labeling or post-correction tasks, for instance as a fix-up layer on top of a Ukrainian tagger. - **Templates are cheap to try:** A useless template does no harm, because none of its instances will be selected. This makes it low-risk to experiment with new feature types, such as morphology-specific conditions for richly inflected languages. - **Lexicon constraint on rewrites:** A rule may change a word's tag only to a tag that word has actually carried in the training data. This simple guard against implausible corrections could be strengthened with an external dictionary. - **Separate data for rule learning:** Rules are induced on a dedicated patch corpus, kept apart from both the lexicon-training data and the test data. This is a clean way to fit a second-stage corrector without overfitting to the base model's training set. - **Learned rules replace hand-written idioms:** The system discovered on its own how to tag "as old as" correctly. Interpretable learned rules could likewise substitute for hand-curated exception lists when moving to a new tag set, genre, or language. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) ## Cited by Cited by 205 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 198 not yet. - [Experiments on Sentence Boundary Detection](https://md.research.elamur.ai/free/en/papers/aclbulk-2000-stevenson-experiments-sentence-boundary-detection/) - [A Survey of Paraphrasing and Textual Entailment Methods](https://md.research.elamur.ai/free/en/papers/androutsopoulos-2009-survey-paraphrasing-textual-entailment-methods/) - [Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging](https://md.research.elamur.ai/free/en/papers/brill-1995-transformation-based-error-driven-learning/) - [Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/harabagiu-2000-multilingual-coreference-resolution/) - [Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach](https://md.research.elamur.ai/free/en/papers/ng-1996-integrating-multiple-knowledge-sources-disambiguate/) - [Lexicon-Based Methods for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/) - [Learning Subjective Language](https://md.research.elamur.ai/free/en/papers/wiebe-2004-learning-subjective-language/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{brill1992simple, title = {{A Simple Rule-Based Part of Speech Tagger}}, author = {Brill, Eric}, booktitle = {Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992}, year = {1992}, doi = {10.3115/974499.974526}, url = {https://doi.org/10.3115/974499.974526} } ``` - [← previous A Practical Part-of-Speech Tagger](https://md.research.elamur.ai/free/en/papers/cutting-1992-practical-part-speech-tagger/) - [next → An Argument for Basic Emotions](https://md.research.elamur.ai/free/en/papers/ekman-1992-argument-basic-emotions/) - [↑ back to papers of 1992](https://md.research.elamur.ai/free/en/papers/1992/) ## References 12 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Church, K. A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text. In Proceedings o/ the Second Conference on Applied Natural Language Processing, ACL, 136-143, 1988. [find](https://scholar.google.com/scholar?q=Church%2C+K%2E+A+Stochastic+Parts+Program+and+Noun+Phrase+Parser+for+Unrestricted+Text%2E+In+Proceedings+o%2F+the+Second+Conference+on+Applied+Natural+Language+Processing%2C+ACL%2C+136%2D143%2C+1988%2E) [our card](https://md.research.elamur.ai/free/en/papers/church-1988-stochastic-parts-program-noun-phrase/) 2. Cutting, D., Kupiec, J., Pederson, J. and Sibun, P. A Practical Part-of-Speech Tagger. In Proceedings of the Third Conference on Applied Natural Language Processing, AUL, 1992. [find](https://scholar.google.com/scholar?q=Cutting%2C+D%2E%2C+Kupiec%2C+J%2E%2C+Pederson%2C+J%2E+and+Sibun%2C+P%2E+A+Practical+Part%2Dof%2DSpeech+Tagger%2E+In+Proceedings+of+the+Third+Conference+on+Applied+Natural+Language+Processing%2C+AUL%2C+1992%2E) [our card](https://md.research.elamur.ai/free/en/papers/cutting-1992-practical-part-speech-tagger/) 3. DeRose, S.J. Grammatical Category Disambiguation by Statistical Optimization. Computational Linguistics 14: 31-39, 1988. [find](https://scholar.google.com/scholar?q=DeRose%2C+S%2EJ%2E+Grammatical+Category+Disambiguation+by+Statistical+Optimization%2E+Computational+Linguistics+14%3A+31%2D39%2C+1988%2E) 4. Deroualt, A. and Merialdo, B. Natural language modeling for phoneme-to-text transcription. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, 742-749, 1986. [find](https://scholar.google.com/scholar?q=Deroualt%2C+A%2E+and+Merialdo%2C+B%2E+Natural+language+modeling+for+phoneme%2Dto%2Dtext+transcription%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2C+Vol%2E+PAMI%2D8%2C+No%2E+6%2C+742%2D749%2C+1986%2E) 5. Francis, W. Nelson and Ku~era, Henry, Frequency analysis of English usage. Lexicon and grammar. Houghton Mifflin, Boston, 1982. 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[find](https://scholar.google.com/scholar?q=Hindle%2C+D%2E+Acquiring+disambiguation+rules+from+text%2E+Proceedings+of+the+PTth+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+1989%2E) 9. Jelinek, F. Markov source modeling of text generation. In J. K. Skwirzinski, ed., Impact of Processing Techniques on Communication, Dordrecht, 1985. [find](https://scholar.google.com/scholar?q=Jelinek%2C+F%2E+Markov+source+modeling+of+text+generation%2E+In+J%2E+K%2E+Skwirzinski%2C+ed%2E%2C+Impact+of+Processing+Techniques+on+Communication%2C+Dordrecht%2C+1985%2E) 10. Klein, S. and Simmons, R.F. A Computational Approach to Grammatical Coding of English Words. JA CM 10: 334-47. 1963. [find](https://scholar.google.com/scholar?q=Klein%2C+S%2E+and+Simmons%2C+R%2EF%2E+A+Computational+Approach+to+Grammatical+Coding+of+English+Words%2E+JA+CM+10%3A+334%2D47%2E+1963%2E) 11. Kupiec, J. Augmenting a hidden Markov model for phrase-dependent word tagging. 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