UPOS (Universal Part-of-Speech) tags are a core component of the Universal Dependencies (UD) project, designed to provide a standardized, fixed set of 17 categories that remain consistent across all human languages. Unlike language-specific systems (XPOS), which reflect the unique morphological intricacies of a single tongue, UPOS focuses on the functional role of a word. By stripping away language-specific "noise," UPOS allows researchers and developers to compare syntactic structures cross-linguistically and facilitates Cross-Lingual Transfer Learning—where an AI model trained on one language (like English) can apply its structural knowledge to another (like Romanian or Korean). It essentially serves as a "Lingua Franca" for computational linguistics, ensuring that a NOUN remains a NOUN whether the underlying grammar is agglutinative, fusional, or analytic.
Try our French UPOS tagging now.
| Group | Tag | Meaning | Example |
|---|---|---|---|
| Open Class | ADJ | Adjective | grand, vieux, vert, incompréhensible, premier |
| ADV | Adverb | très, demain, en bas, où, là-bas | |
| INTJ | Interjection | chut, aïe, bravo, bonjour | |
| NOUN | Noun (common) | fille, chat, arbre, air, beauté | |
| PROPN | Proper Noun | Marie, Jean, Londres, OTAN, HBO | |
| VERB | Verb | courir, court, courant, manger, a mangé, mangé | |
| Closed Class | ADP | Adposition | dans, à, pendant |
| AUX | Auxiliary | est, a (fait), fera, devrait (faire) | |
| CONJ | Conjunction | et, ou, mais (ancienne étiquette) | |
| CCONJ | Coordinating Conjunction | et, ou, mais | |
| SCONJ | Subordinating Conjunction | si, pendant que, que | |
| DET | Determiner | un, une, le, la, les | |
| NUM | Numeral | 1, 2017, un, soixante-dix-sept, MMXIV | |
| PART | Particle | ne... pas, non | |
| PRON | Pronoun | je, tu, il, elle, moi-même, eux-mêmes, quelqu'un | |
| Other | PUNCT | Punctuation | ., (, ), ?, ] |
| SYM | Symbol | $, %, +, −, :), 🐻 | |
| X | Other / Foreign | sfpksdpsxmsa, ..., foreign words | |
| SPACE | Space | newlines, tabs, extra spaces |
XPOS (Language-Specific Part-of-Speech) tagging offers a much higher level of granularity than the broader UPOS (Universal Part-of-Speech) system. While UPOS provides a standardized set of labels designed to work consistently across every language—ensuring that a NOUN in English is treated similarly to a NOUN in XPOS preserves the unique "linguistic DNA" of a specific language. It is the engine behind complex morphological analysis, allowing a system to distinguish not just that a word is a "Verb," but specifically that it is a "Third-Person, Singular, Past Tense, Passive Voice" verb. By capturing the deep grammatical details that UPOS omits for the sake of universality, XPOS enables the creation of translation tools and parsers that understand the precise inflectional logic of a specific culture and tongue.
In French, Spanish, Portuguese, Danish, Norwegian, Russian, Hebrew, Catalan, Finnish, Sanskrit, Thai and Ukrainian, a separate fine-grained XPOS tagset is not defined. Instead, these languages utilize UPOS with specific granularities stored within Morphological Features.
Try our French XPOS tagging now.
| Group | Category | Label | Meaning | Example |
|---|---|---|---|---|
| Nominal | Gender & Animacy | Masc | Masculine | perro (dog) |
| Fem | Feminine | perra (female dog) | ||
| Neut | Neuter | ello (it/that) | ||
| Com | Common | estudiante | ||
| Hum | Human | persona, qui | ||
| Anim | Animate | (Living entity) | ||
| Inan | Inanimate | (Object) | ||
| Definite & Degree | Def | Definite | le, la, el | |
| Ind | Indefinite | un, une | ||
| Pos | Positive degree | bueno, bon | ||
| Cmp | Comparative | más, plus | ||
| Sup | Superlative | buenísimo | ||
| Nominal | Number | Sing | Singular | livre (book) |
| Plur | Plural | livres (books) | ||
| Nominal | Case | Nom | Nominative | yo, I |
| Acc | Accusative | me, lo | ||
| Dat | Dative | le, me | ||
| Gen | Genitive | (Possessive case) | ||
| Nominal | NounType & NameType | Class | Classifier (NounType) | ตัว (body/animal) |
| Giv | Given Name (NameType) | สมชาย (Somchai) | ||
| Sur | Surname (NameType) | ใจดี (Jaidee) | ||
| Geo | Geographical (NameType) | กรุงเทพฯ (Bangkok) | ||
| Nat | Nationality (NameType) | ไทย (Thai) | ||
| Com | Company (NameType) | กูเกิล (Google) | ||
| Verbal | Mood & Aspect | Ind | Indicative | yo hablo |
| Sub | Subjunctive | que yo hable | ||
| Imp | Imperative | ¡habla! | ||
| Cnd | Conditional | hablaría | ||
| Imp | Imperfective | hablaba | ||
| Perf | Perfective | hablé | ||
| Prog | Progressive | estoy hablando | ||
| Verbal | Person & Politeness | 1 | First Person | yo, nosotros |
| 2 | Second Person | tú, vosotros | ||
| 3 | Third Person | él, ella | ||
| Form | Polite/Formal | Usted, Vous | ||
| Infm | Informal | tú, toi | ||
| Verbal | Tense | Pres | Present | mange, eat |
| Past | Past | mangé, ate | ||
| Fut | Future | mangerai | ||
| Verbal | VerbForm & Voice | Fin | Finite | il court |
| Inf | Infinitive | courir, to run | ||
| Part | Participle | vu, visto | ||
| Ger | Gerund | corriendo | ||
| Act | Active Voice | veo (I see) | ||
| Pass | Passive Voice | soy visto | ||
| Lexical | NumType | Card | Cardinal | uno, deux |
| Ord | Ordinal | primero, 1er | ||
| Mult | Multiplicative | doble, triple | ||
| PronType | Prs | Personal | yo, je | |
| Dem | Demonstrative | este, celui | ||
| Rel | Relative | que, qui | ||
| Int | Interrogative | ¿quién?, qui? | ||
| Lexical | Polarity & Poss | Neg | Negative | no, pas |
| Yes | Possessive | mio, sien | ||
| Yes | Reflexive | se, me, te | ||
| Lexical | PartType (Particles) | Enp | Ending Particle | ครับ (krab), ค่ะ (kha) |
| Res | Response Particle | ใช่ (chai / yes) | ||
| Int | Interrogative Particle | ไหม (mai / ?) | ||
| Special | Other | Yes | Foreign Word | software, ad-hoc |
| Yes | Abbreviation | etc., adj. | ||
| Special | Word Formation | Yes (Prefix) | Nominalizing Prefix | การ- (kan-), ความ- (khwam-) |
| Rdp (Echo) | Reduplicative | เด็กๆ (dek-dek) |
The DEP (Syntactic Dependency) refers to the specific grammatical relationship between a "child" token and its "head" (parent) token. While primary labels (like nsubj or obj) describe the basic structure, attachments starting with a colon (:) provide fine-grained sub-type information. For instance, while nsubj identifies a subject, :pass refines this to show the subject is being acted upon (Passive Voice). Similarly, :nn (Noun Compound) or :assmod (Associative Modifier) help the parser distinguish between simple modifiers and complex ownership or compound relationships, allowing for a much deeper "logical" understanding of the sentence.
| Category | Label | Meaning | Example (Token in bold) |
|---|---|---|---|
| Core Arguments | nsubj | Nominal subject | Elon mange. |
| csubj | Clausal subject | Ce qu'il a fait était mal. | |
| obj | Direct object | Je vois la lune. | |
| iobj | Indirect object | Elle m'a donné un cadeau. | |
| ccomp | Clausal complement (finite) | Il a dit qu'il était fatigué. | |
| xcomp | Open clausal complement | Je veux partir. | |
| Non-Core Dependents | obl | Oblique nominal | Il s'est assis sur la chaise. |
| vocative | Vocative | Jean, viens ici ! | |
| expl | Expletive | Il y a un chat. | |
| dislocated | Dislocated element | Cet homme, je le connais. | |
| advcl | Adverbial clause modifier | Je suis parti après son arrivée. | |
| advmod | Adverbial modifier | Cours vite. | |
| discourse | Discourse element | Eh bien, je ne suis pas sûr. | |
| aux | Auxiliary | Je peux voir. | |
| cop | Copula | Elle est heureuse. | |
| mark | Subordinating marker | Je sais que tu sais. | |
| Nominal Dependents | nmod | Nominal modifier | La porte de la voiture. |
| appos | Appositional modifier | Sam, mon ami. | |
| nummod | Numeric modifier | Sept jours. | |
| acl | Adjectival clause | Le plan pour gagner. | |
| amod | Adjectival modifier | Le ciel bleu. | |
| det | Determiner | La fin. | |
| case | Case marking | Le roi de France. | |
| fixed | Fixed multiword expression | Malgré cela. | |
| flat | Flat multiword name | La ville de Paris. | |
| compound | Compound noun | Cabine téléphonique. | |
| list | List element | Téléphone, clés, portefeuille. | |
| Coordination | conj | Conjunct | Du pain et du beurre. |
| cc | Coordinating conjunction | Du pain et du beurre. | |
| Special Labels | aux:pass | Passive auxiliary | Il a été volé. |
| punct | Punctuation | Bonjour ! | |
| dep | Unspecified dependency | (Utilisé pour les liens inconnus) | |
| ROOT | Root of the sentence | J'ai déjeuné. |
| Attachment | Full Name | Explanation | Example |
|---|---|---|---|
| :pass | Passive | Indicates a relationship in a passive voice construction. | nsubj:pass (La fenêtre a été brisée) |
| :nn | Noun Compound | Indicates that a noun is modifying another noun in a compound structure. | compound:nn (Chargeur de téléphone) |
| :prep | Prepositional | Refines a modifier governed specifically by a preposition. | nmod:prep (Le chat sur le tapis) |
| :assmod | Associative Modifier | Common in Romanian/Baltic languages; shows nouns modifying other nouns. | nmod:assmod (La voiture de mon père) |
| :poss | Possessive | Indicates ownership or a possessive relationship. | nmod:poss (Mon chien, le chapeau de Jean) |
| :relcl | Relative Clause | Identifies a clause that modifies a noun phrase. | acl:relcl (Le livre que j'ai lu) |
| :tmod | Temporal Modifier | A modifier specifically describing time or duration. | nmod:tmod (Je pars mardi) |
| :prt | Particle | Used for phrasal verb particles. | compound:prt (Abandonner, s'éteindre) |
| :rcomp | Relative Complement | Used for complements of relative clauses (common in Dutch). | advcl:rcomp (L'homme qui est parti) |
| :flat | Flat Modifier | Used for multi-word expressions that don't have a clear internal head. | flat:name (Le président Obama) |
NER (Named Entity Recognition) is a Natural Language Processing (NLP) task that automatically identifies and categorizes key information (entities) in a text into predefined classes. In spaCy, the statistical model "looks" at the context of a word to determine if it refers to a person, an organization, a monetary value, or a specific date. This is crucial for extracting structured data from unstructured text, such as finding all the company names mentioned in a news article or identifying the dates of events in a history book.
Comparison Note: GPE vs. LOC
Determining whether a place is a GPE or a LOC depends on its political nature:
GPE (Geopolitical Entity): If the location has a government, specific laws, or human-defined administrative borders, it is labeled as a GPE. Examples include Seoul, Germany, the United Kingdom, and California.
LOC (Location): If the place is a natural physical feature or a broad geographic region without a singular governing body, it is labeled as a LOC. Examples include the Alps, the Pacific Ocean, the Middle East, and Mount Everest.
| Label | Meaning | Example |
|---|---|---|
| 🌍 GPE | Geopolitical entity (countries, cities, states) | France, Paris, Canada, Californie |
| 🏔️ LOC | Non-political location (mountains, rivers) | Océan Pacifique, Mont Everest, Les Alpes |
| 🏢 FAC | Facility (buildings, airports, highways) | Tour Eiffel, Aéroport Charles de Gaulle, Burj Khalifa |
| 👤 PERSON | People (real or fictional) | Elon Musk, Harry Potter, Alan Turing |
| 🚩 NORP | Nationalities, religious or political groups | Américain, Bouddhiste, Démocrates, Japonais |
| 🏢 ORG | Organizations (companies, institutions) | Google, Nations Unies, Apple, FIFA |
| 📅 DATE | Absolute or relative dates | 14 juillet, 2026, hier, la semaine prochaine |
| ⌚ TIME | Times smaller than a day | 9h30, coucher de soleil, dix minutes |
| 🎊 EVENT | Named events (wars, festivals) | Seconde Guerre mondiale, Coachella, Jeux Olympiques |
| 💰 MONEY | Monetary values, including unit | 100 $, 5 millions d'euros, 50 £ |
| ‱ PERCENT | Percentage, including "%" | 20 %, quatre-vingts pour cent, 0,5 % |
| ⚖️ QUANTITY | Measurements (weight, distance) | 5 km, 50 kg, 30 mètres carrés |
| 🔢 ORDINAL | "First", "second", etc. | premier, 2e, neuvième |
| 🔢 CARDINAL | Numbers not classified elsewhere | 10, mille, trois |
| 📦 PRODUCT | Objects, vehicles, foods, etc. (not services) | iPhone, Tesla Model S, Coca-Cola |
| 🎨 WORK_OF_ART | Titles of books, songs, etc. | La Joconde, Bohemian Rhapsody, Hamlet |
| 📜 LAW | Named legal documents | La Constitution, Traité de Versailles |
| 🗣️ LANGUAGE | Named languages | Français, Python, Mandarin |
Si nous traitons la phrase « Google a son siège en Californie », les différentes couches ressemblent à ceci :
Lemma (Lemme): "Google", "avoir", "son", "siège", "en", "Californie"
UPOS (Catégories grammaticales universelles): "PROPN(Nom propre)", "VERB(Verbe)", "DET(Déterminant)", "NOUN(Nom)", "ADP(Préposition)", "PROPN(Nom propre)"
XPOS (Caractéristiques morphologiques): "PROPN", "VERB(Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin)", "DET(Gender=Masc|Number=Sing|Poss=Yes|PronType=Prs)", "NOUN(Gender=Masc|Number=Sing)", "ADP", "PROPN(Gender=Fem|Number=Sing)"
DEP (Relations de dépendance): « Google » est le nsubj (sujet nominal) du verbe « a » qui est la Root (racine de la phrase). « siège » est l' obj (objet direct du verbe). « Californie » est un obl (complément circonstanciel) lié par la préposition « en ».
NER (Reconnaissance des entités nommées): « Google » est une 🏢 ORG (Organisation), « Californie » est une 🌍 GPE (Entité géopolitique).
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