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, viejo, verde, incomprensible, primero
ADV Adverb muy, mañana, abajo, donde, allá
INTJ Interjection psst, ay, bravo, hola
NOUN Noun (common) niña, gato, árbol, aire, belleza
PROPN Proper Noun Mary, John, Londres, OTAN, HBO
VERB Verb corre, corre, corre, come, comió, comió
Closed Class ADP Adposition en, a, durante
AUX Auxiliary es, ha (hecho), haré (hará), debería (hacer)
CONJ Conjunction y, o, pero (etiqueta heredada)
CCONJ Coordinating Conjunction y, o, pero
SCONJ Subordinating Conjunction si, mientras, eso
DET Determiner un, un, el
NUM Numeral 1, 2017, uno, setenta y siete, MMXIV
PART Particle , no
PRON Pronoun Yo, tú, él, ella, yo mismo, ellos mismos, alguien
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.

DEP Full Syntactic Dependency Labels
Category Label Meaning Example (Token in bold)
Core Arguments nsubj Nominal subject Elon come.
csubj Clausal subject Lo que hizo estuvo mal.
obj Direct object Veo la luna.
iobj Indirect object Ella me dio un regalo.
ccomp Clausal complement (finite) Dijo que estaba cansado.
xcomp Open clausal complement Quiero ir.
Non-Core Dependents obl Oblique nominal Se sentó en la silla.
vocative Vocative John, ¡ven aquí!
expl Expletive Hay un gato.
dislocated Dislocated element Ese hombre, lo conozco.
advcl Adverbial clause modifier Me fui después de que él llegó.
advmod Adverbial modifier Corre rápido.
discourse Discourse element Bueno, no estoy seguro.
aux Auxiliary Puedo puedo ver.
cop Copula Ella está feliz.
mark Subordinating marker que tú lo sabes.
Nominal Dependents nmod Nominal modifier La puerta del auto.
appos Appositional modifier Sam, mi amigo.
nummod Numeric modifier Siete días.
acl Adjectival clause El plan para ganar.
amod Adjectival modifier El cielo azul.
det Determiner El fin.
case Case marking El rey de Francia.
fixed Fixed multiword expression A pesar de eso.
flat Flat multiword name Ciudad de Nueva York.
compound Compound noun Cabina telefónica.
list List element Teléfono, llaves, billetera.
Coordination conj Conjunct Pan y mantequilla.
cc Coordinating conjunction Pan y mantequilla.
Special Labels aux:pass Passive auxiliary Fue robado.
punct Punctuation ¡Hola!
dep Unspecified dependency (Usado para enlaces desconocidos)
ROOT Root of the sentence Yo comí el almuerzo.

Common Dependency Attachments (Sub-labels)
Attachment Full Name Explanation Example
:pass Passive Indicates a relationship in a passive voice construction. nsubj:pass (La ventana estaba rota)
:nn Noun Compound Indicates that a noun is modifying another noun in a compound structure. compuesto:nn (cargador de teléfono)
:prep Prepositional Refines a modifier governed specifically by a preposition. nmod:prep (El gato en la alfombra)
:assmod Associative Modifier Common in Romanian/Baltic languages; shows nouns modifying other nouns. nmod:assmod (El coche de mi padre)
:poss Possessive Indicates ownership or a possessive relationship. nmod:poss (Mi perro, el sombrero de John)
:relcl Relative Clause Identifies a clause that modifies a noun phrase. acl:relcl (El libro que leí)
:tmod Temporal Modifier A modifier specifically describing time or duration. nmod:tmod (Me voy el martes)
:prt Particle Used for phrasal verb particles. compuesto:prt (Ríndete arriba, cierra apagado)
:rcomp Relative Complement Used for complements of relative clauses (common in Dutch). advcl:rcomp (El hombre que se fue)
:flat Flat Modifier Used for multi-word expressions that don't have a clear internal head. plano:nombre (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) Estados Unidos, Nueva York, Francia, California
🏔️ LOC Non-political location (mountains, rivers) Océano Pacífico, Monte Everest, Los Alpes
🏢 FAC Facility (buildings, airports, highways) Puente Golden Gate, aeropuerto JFK, Burj Khalifa
👤 PERSON People (real or fictional) Elon Musk, Harry Potter, Alan Turing
🚩 NORP Nationalities, religious or political groups Americanos, budistas, demócratas, japoneses
🏢 ORG Organizations (companies, institutions) Google, Naciones Unidas, Apple, FIFA
📅 DATE Absolute or relative dates 4 de julio de 2026, ayer, la próxima semana
⌚ TIME Times smaller than a day 9:30 a.m., atardecer, diez minutos
🎊 EVENT Named events (wars, festivals) Segunda Guerra Mundial, Coachella, Juegos Olímpicos
💰 MONEY Monetary values, including unit 100 dólares, 5 millones de euros, 50 libras
‱ PERCENT Percentage, including "%" 20%, ochenta por ciento, 0,5%
⚖️ QUANTITY Measurements (weight, distance) 5 km, 100 libras, 30 metros cuadrados
🔢 ORDINAL "First", "second", etc. primero, segundo, noveno
🔢 CARDINAL Numbers not classified elsewhere 10, mil, tres
📦 PRODUCT Objects, vehicles, foods, etc. (not services) iPhone, Tesla Modelo S, Coca-Cola
🎨 WORK_OF_ART Titles of books, songs, etc. Mona Lisa, Bohemian Rhapsody, Hamlet
📜 LAW Named legal documents La Constitución, Tratado de Versalles
🗣️ LANGUAGE Named languages Inglés, Python, Mandarín

Ejemplo de PNL (NLP Example)

Si procesamos la frase "Google tiene su sede en California", las capas se ven así:

Lema: "Google", "be", "base", "in", "California"
UPOS: "PROPN(Proper Noun)", "AUX(Auxiliary)", "VERB(Verb)", "ADP(Adposition)", "PROPN(Proper Noun)"
XPOS: "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" es el nsubj (sujeto nominal) del verbo "based" que es Root (raíz de la oración).
NER: "Google" es una 🏢 ORG (Organización), "California" es una 🌍 GPE (Entidad Geopolítica).

Part-of-Speech for Main Languages

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