Universal POS, Detailed POS, NER, DEP

UPOS (Universal POS)

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.

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UPOS Universal Part-of-Speech
Group Tag Meaning Example
Open Class ADJ Adjective grande, velho, verde, incompreensível, primeiro
ADV Adverb muito, amanhã, embaixo, onde, ali
INTJ Interjection psst, ai, bravo, olá
NOUN Noun (common) menina, gato, árvore, ar, beleza
PROPN Proper Noun Mary, John, Londres, OTAN, HBO
VERB Verb corre, corre, corre, come, comeu, comeu
Closed Class ADP Adposition dentro, para, durante
AUX Auxiliary é, tem (feito), irá (fazer), deveria (fazer)
CONJ Conjunction e, ou, mas (tag herdada)
CCONJ Coordinating Conjunction e, ou, mas
SCONJ Subordinating Conjunction se, enquanto, isso
DET Determiner um, um, o
NUM Numeral 1º de janeiro de 2017, um, setenta e sete, MMXIV
PART Particle de, não
PRON Pronoun Eu, você, ele, ela, eu mesmo, eles mesmos, alguém
Other PUNCT Punctuation ., (, ), ?, ]
SYM Symbol $, %, +, −, :), 🐻
X Other / Foreign sfpksdpsxmsa, ..., foreign words
SPACE Space newlines, tabs, extra spaces

XPOS (Detailed POS)

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.

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General xpos Morphological Details
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)

Dependency

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.

Cabine
DEP Full Syntactic Dependency Labels
Category Label Meaning Example (Token in bold)
Core Arguments nsubj Nominal subject Elon come.
csubj Clausal subject O que ele fez foi errado.
obj Direct object Vejo a lua.
iobj Indirect object Ela me me deu um presente.
ccomp Clausal complement (finite) Ele disse que estava cansado.
xcomp Open clausal complement Quero ir.
Non-Core Dependents obl Oblique nominal Ele sentou na cadeira.
vocative Vocative João, venha aqui!
expl Expletive um gato.
dislocated Dislocated element Esse homem, eu o conheço.
advcl Adverbial clause modifier Saí depois que ele chegou.
advmod Adverbial modifier Corra rápido.
discourse Discourse element Bem, não tenho certeza.
aux Auxiliary Eu posso ver.
cop Copula Ela está feliz.
mark Subordinating marker Eu sei que você sabe.
Nominal Dependents nmod Nominal modifier A porta do carro.
appos Appositional modifier Sam, meu amigo.
nummod Numeric modifier Sete dias.
acl Adjectival clause O plano para vencer.
amod Adjectival modifier O céu azul.
det Determiner O fim.
case Case marking O rei da França.
fixed Fixed multiword expression Apesar disso.
flat Flat multiword name Nova York.
compound Compound nountelefone.
list List element Telefone, chaves, carteira.
Coordination conj Conjunct Pão e manteiga.
cc Coordinating conjunction Pão e manteiga.
Special Labels aux:pass Passive auxiliary Foi foi roubado.
punct Punctuation Olá!
dep Unspecified dependency (Usado para links desconhecidos)
ROOT Root of the sentence Eu almoce.

Common Dependency Attachments (Sub-labels)
Attachment Full Name Explanation Example
:pass Passive Indicates a relationship in a passive voice construction. nsubj:pass (A janela estava quebrada)
:nn Noun Compound Indicates that a noun is modifying another noun in a compound structure. composto:nn (carregador de telefone)
:prep Prepositional Refines a modifier governed specifically by a preposition. nmod:prep (O gato no tapete)
:assmod Associative Modifier Common in Romanian/Baltic languages; shows nouns modifying other nouns. nmod:assmod (O carro do meu pai)
:poss Possessive Indicates ownership or a possessive relationship. nmod:poss (Meu cachorro, chapéu do John)
:relcl Relative Clause Identifies a clause that modifies a noun phrase. acl:relcl (O livro que li)
:tmod Temporal Modifier A modifier specifically describing time or duration. nmod:tmod (Vou embora terça-feira)
:prt Particle Used for phrasal verb particles. composto:prt (Desistir , encerrar desligar)
:rcomp Relative Complement Used for complements of relative clauses (common in Dutch). advcl:rcomp (O homem que saiu)
:flat Flat Modifier Used for multi-word expressions that don't have a clear internal head. flat:nome (Presidente Obama)

Named Entity Recognition

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.

NER Named Entity Recognition
Label Meaning Example
🌍 GPE Geopolitical entity (countries, cities, states) EUA, Nova York, França, Califórnia
🏔️ LOC Non-political location (mountains, rivers) Oceano Pacífico, Monte Everest, Alpes
🏢 FAC Facility (buildings, airports, highways) Ponte Golden Gate, Aeroporto JFK, Burj Khalifa
👤 PERSON People (real or fictional) Elon Musk, Harry Potter, Alan Turing
🚩 NORP Nationalities, religious or political groups Americanos, Budistas, Democratas, Japoneses
🏢 ORG Organizations (companies, institutions) Google, Nações Unidas, Apple, FIFA
📅 DATE Absolute or relative dates 4 de julho de 2026, ontem, semana que vem
⌚ TIME Times smaller than a day 9h30, pôr do sol, dez minutos
🎊 EVENT Named events (wars, festivals) Segunda Guerra Mundial, Coachella, Jogos Olímpicos
💰 MONEY Monetary values, including unit US$ 100, 5 milhões de euros, £ 50
‱ PERCENT Percentage, including "%" 20%, oitenta por cento, 0,5%
⚖️ QUANTITY Measurements (weight, distance) 5 km, 100 libras, 30 metros quadrados
🔢 ORDINAL "First", "second", etc. primeiro, 2º, nono
🔢 CARDINAL Numbers not classified elsewhere 10, mil, três
📦 PRODUCT Objects, vehicles, foods, etc. (not services) iPhone, Tesla Model S, Coca-Cola
🎨 WORK_OF_ART Titles of books, songs, etc. Mona Lisa, Rapsódia Boêmia, Hamlet
📜 LAW Named legal documents A Constituição, Tratado de Versalhes
🗣️ LANGUAGE Named languages Inglês, Python, Mandarim

Exemplo de PNL (NLP Example)

Se processarmos a frase “O Google tem sede na Califórnia”, as camadas ficarão assim:

Lema: "Google", "be", "base", "in", "California"
UPOS: "PROPN(Proper Noun)", "AUX(Auxiliary)", "VERB(Verb)", "ADP(Adposition)", "PROPN(Proper Noun)"
XPPOS: "NNP(Proper noun, singular)", "VBZ(Verb, 3rd person singular present)", "VBN(Verb, past participle)", "IN(Preposition or subordinating conjunction)", "NNP(Proper noun, singular)"
DEP: "Google" é o nsubj (sujeito nominal) do verbo "baseado" que é Raiz (Raiz da frase).
NER: "Google" é uma 🏢 ORG (Organização), "Califórnia" é uma 🌍 GPE (Entidade Geopolítica).

Part-of-Speech for Main Languages

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