--- title: "PROPN: proper noun — definition and key papers" description: "PROPN (proper noun) is the Universal Dependencies part-of-speech tag for nouns, or noun-like words, that name a specific individual, place…" canonical: "https://research.elamur.ai/free/en/concepts/upos-propn/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/concepts/upos-propn/) [home](https://md.research.elamur.ai/free/en/) / [concepts](https://md.research.elamur.ai/free/en/concepts/) / PROPN: proper noun # 🏷️ PROPN: proper noun Universal Dependencies · 7 key papers · [all concepts](https://md.research.elamur.ai/free/en/concepts/) Part of: [🗨️ Natural language processing](https://md.research.elamur.ai/free/en/concepts/natural-language-processing/) · [🌐 Universal Dependencies](https://md.research.elamur.ai/free/en/concepts/universal-dependencies/) PROPN (proper noun) is the Universal Dependencies part-of-speech tag for nouns, or noun-like words, that name a specific individual, place, organization or other unique entity rather than a class, such as Paris, Marie or Toyota. Also: proper noun, proper name, NNP/NNPS (Penn Treebank), UPOS PROPN ## What it is In [Universal Dependencies](https://md.research.elamur.ai/free/en/concepts/universal-dependencies/) (UD), every word gets one tag from a closed list of 17 universal part-of-speech (UPOS) categories ([Nivre et al. 2020](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/)). PROPN is the tag for names. A common noun like *city* applies to any member of a class. A proper noun like *Lyon* refers to one particular entity. Proper nouns refer without describing: what they pick out does not follow from a dictionary sense of the word. In many writing systems they are capitalized. Capitalization is only a hint, though, not the test. UD treats nominals as the units that denote entities ([Marneffe et al. 2021](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies/)). PROPN marks the subset of nominals whose job is to name. That makes it a useful signal for [named entity recognition](https://md.research.elamur.ai/free/en/concepts/named-entity-recognition/) and [coreference resolution](https://md.research.elamur.ai/free/en/concepts/coreference-resolution/). ### Origin in the universal tagset The universal tagset of Petrov, Das and McDonald had twelve coarse categories with no separate tag for names. Proper and common nouns were merged under NOUN ([Petrov et al. 2012](https://md.research.elamur.ai/free/en/papers/petrov-2012-universal-part-speech-tagset/)). UD v1 extended and revised that set to 17 tags, and PROPN, together with AUX, was among the additions ([Nivre et al. 2016](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/)). The split restores a distinction that many source tagsets already drew, such as NNP in the Penn Treebank. ### Borderline cases - PROPN vs NOUN: common nouns inside a name (*bank* in *Bank of England*) are handled differently across treebanks. Some tag every part of the name PROPN; others keep the word's ordinary category. - PROPN vs ADJ: adjectives derived from names (English *French*, German *deutsch*) are ADJ, not PROPN. - PROPN vs X: foreign-language material that is not used as a name falls under X. UD v2 also offers the Foreign feature for such words ([Nivre et al. 2020](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/)). - Months, weekdays and genericized brand names (*a hoover*): whether these are PROPN or NOUN varies by language and treebank. ### Features and relations Proper nouns carry the nominal features of their language: Number, and where relevant Gender and Case. Abbr=Yes marks abbreviations of any kind, acronyms such as *NATO* included ([Nivre et al. 2020](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/)). The feature inventory descends from Interset, the interlingua of morphosyntactic features designed for tagset conversion ([Zeman 2008](https://md.research.elamur.ai/free/en/papers/zeman-2008-reusable-tagset-conversion-tagset-drivers/); [Nivre et al. 2016](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/)). Syntactically, a proper noun can fill any nominal role. Examples are subject (nsubj), object (obj), oblique (obl) and nominal modifier (nmod). Multiword names are joined with flat, the relation for multiword expressions without internal head structure. In UD v2 it is often subtyped as flat:name. The first word of the name is the head, and the following words attach to it. English names also often stack as noun modifiers (*Obama administration*). The compound relation, used for noun–noun compounding, captures this. In English, the possessive *'s* is split off as a separate word, so the name itself keeps its plain form ([Marneffe et al. 2021](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies/)). ## Examples - English: *Marie/PROPN Curie/PROPN moved to Paris/PROPN.* Here *Marie* is the head of the name, *Curie* attaches to it by flat (or flat:name), and *Paris* is obl. - French: *Jean-Paul Sartre est né à Paris.* Both name parts are PROPN. - German: *Angela Merkels Rede.* Where case is annotated on names, the token *Merkels* carries Case=Gen. - Czech: *v Praze* ('in Prague'). The [lemma](https://md.research.elamur.ai/free/en/concepts/lemma/) is *Praha*, and the form carries Case=Loc. - Finnish: *Helsingissä* ('in Helsinki'). The PROPN takes the inessive case ending. ## Across languages Languages differ mainly in how much [morphology](https://md.research.elamur.ai/free/en/concepts/morphology/) proper nouns carry, and in whether the script gives cues. - In Czech or Finnish, names decline like other nouns, so a PROPN token rarely equals its lemma. - English and French mark names mostly by capitals. - Chinese, Japanese, Arabic and Hebrew scripts have no capitals. Name detection then rests on context, which can make PROPN one of the harder tags in [part-of-speech tagging](https://md.research.elamur.ai/free/en/concepts/part-of-speech-tagging/) for these languages. ## In treebanks and research The boundary between PROPN and NOUN depends on annotator decisions. It is therefore a common source of inconsistency within and across [treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/). Systematic checks of UD corpora have looked for annotation inconsistencies in general ([Marneffe et al. 2017](https://md.research.elamur.ai/free/en/papers/marneffe-2017-assessing-annotation-consistency-universal-dependencies/)). One proposed measure compares UPOS trigram distributions between two treebanks of the same language. It builds on the asymmetric KLcpos3 measure of Rosa and Žabokrtský ([Aggarwal & Zeman 2020](https://md.research.elamur.ai/free/en/papers/aggarwal-2020-estimating-pos-annotation-consistency-different/)): ``` θpos(A,B) = KLcpos3(A,B) + KLcpos3(B,A) ``` In this formula, A and B are the two treebanks. KLcpos3 is a Kullback–Leibler divergence over trigrams of universal POS tags. A trigram missing from one treebank is given a count of one, so the divergence stays defined. Summing both directions yields a symmetric score, where a low value suggests consistent annotation. A score of 0.5 or less counts as consistent whatever the genre mix ([Aggarwal & Zeman 2020](https://md.research.elamur.ai/free/en/papers/aggarwal-2020-estimating-pos-annotation-consistency-different/)). The score also rises with genre differences and with smaller data. Disagreements over whether names are PROPN or NOUN may therefore contribute to such divergences, but the measure does not isolate them ([Aggarwal & Zeman 2020](https://md.research.elamur.ai/free/en/papers/aggarwal-2020-estimating-pos-annotation-consistency-different/)). For multilingual [dependency parsing](https://md.research.elamur.ai/free/en/concepts/dependency-parsing/), the coarse universal tags made direct transfer of delexicalized parsers across languages workable ([Petrov et al. 2012](https://md.research.elamur.ai/free/en/papers/petrov-2012-universal-part-speech-tagset/)). All UD languages share the same tag inventory, PROPN included. This supports training multilingual models and cross-lingual transfer ([Nivre et al. 2020](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/)). How often the tag PROPN occurs: its share of all tokens Counted by code over the Universal Dependencies 2.18 treebanks of English and French (every token once, subtypes with their base relation). **The numbers** | Treebanks | Share | | --- | --- | | English treebanks | 5.62% of tokens (63,265 tokens) | | French treebanks | 4.84% of tokens (52,730 tokens) | The relations PROPN words take most often The five most frequent in each language's treebanks, by tokens. **The numbers** | Pair | Tokens | | --- | --- | | English: nmod | 17,048 | | English: flat | 8,249 | | English: nsubj | 7,600 | | English: obl | 7,307 | | English: compound | 7,239 | | French: nmod | 15,000 | | French: flat | 11,976 | | French: nsubj | 8,044 | | French: obl | 6,960 | | French: conj | 3,908 | ## Key papers The works this page cites, the most cited first; citations as counted by OpenAlex plus the papers of this library that cite the work, read 2026-10-24. - [Universal Dependencies v1: A Multilingual Treebank Collection](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/) — Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter et al. · 2016 · 1,741 citations - [A Universal Part-of-Speech Tagset](https://md.research.elamur.ai/free/en/papers/petrov-2012-universal-part-speech-tagset/) — Slav Petrov, Dipanjan Das, Ryan McDonald · 2012 · 1,180 citations - [Universal Dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies/) — Marie-Catherine de Marneffe, Christopher D. Manning, Joakim Nivre et al. · 2021 · 480 citations - [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/) — Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter et al. · 2020 · 325 citations - [Reusable Tagset Conversion Using Tagset Drivers](https://md.research.elamur.ai/free/en/papers/zeman-2008-reusable-tagset-conversion-tagset-drivers/) — Daniel Zeman · 2008 · 181 citations - [Assessing the Annotation Consistency of the Universal Dependencies Corpora](https://md.research.elamur.ai/free/en/papers/marneffe-2017-assessing-annotation-consistency-universal-dependencies/) — Marie-Catherine de Marneffe, Matias Grioni, Jenna Kanerva et al. · 2017 · 30 citations - [Estimating POS Annotation Consistency of Different Treebanks in a Language](https://md.research.elamur.ai/free/en/papers/aggarwal-2020-estimating-pos-annotation-consistency-different/) — Akshay Aggarwal, Daniel Zeman · 2020 · 7 citations ## Related concepts - [Universal Dependencies](https://md.research.elamur.ai/free/en/concepts/universal-dependencies/) — Universal Dependencies is a framework and an open collection of treebanks that annotates the grammar of many languages with one shared… - [Part-of-speech tagging](https://md.research.elamur.ai/free/en/concepts/part-of-speech-tagging/) — Part-of-speech tagging is the task of assigning each token in a text a grammatical category, such as noun, verb, adjective or determiner… - [Morphology](https://md.research.elamur.ai/free/en/concepts/morphology/) — Morphology is the branch of linguistics that studies the internal structure of words. It covers how words are built from meaningful units… ## More papers Topics: [🔤 Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) · [🌐 Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) Shelves: [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) - [← previous concept PRON: pronoun](https://md.research.elamur.ai/free/en/concepts/upos-pron/) - [next concept → Psycholinguistics](https://md.research.elamur.ai/free/en/concepts/psycholinguistics/) - [↑ back to concepts](https://md.research.elamur.ai/free/en/concepts/)