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.

Try our Italian UPOS tagging now.

UPOS Universal Part-of-Speech
Group Tag Meaning Example
Open Class ADJ Adjective grande, vecchio, verde, incomprensibile, primo
ADV Adverb molto, domani, giù, dove, lì
INTJ Interjection pst, ahi, bravo, ciao
NOUN Noun (common) ragazza, gatto, albero, aria, bellezza
PROPN Proper Noun Maria, Giovanni, Londra, NATO, HBO
VERB Verb correre, corre, correndo, mangiare, mangiò, mangiato
Closed Class ADP Adposition in, a, durante
AUX Auxiliary è, ha (fatto), farà, dovrebbe
CONJ Conjunction e, o, ma (tag legacy)
CCONJ Coordinating Conjunction e, o, ma
SCONJ Subordinating Conjunction se, mentre, che
DET Determiner un, una, il, la
NUM Numeral 1, 2017, uno, settantasette, MMXIV
PART Particle —, non
PRON Pronoun io, tu, lui, lei, me stesso, se stessi, qualcuno
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.

Italian XPOS tags follow the ISDT standard. While Italian is a highly inflected Romance language, the XPOS labels are relatively concise. The complexity is often handled by articulated forms; for example, E_RD indicates a preposition (E) fused with a definite article (RD). Unlike the Slavic systems that use a single long string, Italian morphology for gender and number is typically stored in separate attributes, while the XPOS identifies the specific functional subtype of the word.

Try our Italian XPOS tagging now.

Italian xpos tags (ISDT Tagset)
Category Tag Meaning Example
Nouns S Common Noun (Sostantivo) casa, libro, gatto
SP Proper Noun (Nome proprio) Italia, Mario, Roma
Verbs V Main Verb (Verbo principale) mangia, corre, vede
VA Auxiliary Verb (Avere / Essere) ha (fatto), è (andato)
VM Modal Verb (Dovere, Potere, Volere) può, deve, vuole
Modifiers A Adjective bello, grande, veloce
AP Possessive Adjective mio, tuo, suo
B Adverb bene, molto, ieri
Pronouns PP Personal Pronoun io, tu, noi
PC Clitic Pronoun (Complement) lo, la, mi, ti
PD Demonstrative Pronoun questo, quello
PI Indefinite Pronoun qualcuno, nessuno
PR Relative Pronoun che, cui, il quale
PQ Interrogative Pronoun chi?, cosa?
RI Reflexive Clitic si, ci, vi
Adpositions & Articles RD Definite Article il, lo, la, i, gli, le
RI Indefinite Article un, uno, una
E Simple Preposition di, a, da, in, con, su, per, tra, fra
E_RD Articulated Preposition del, nella, sui, dagli
Others CC Coordinating Conjunction e, o, ma
CS Subordinating Conjunction perché, se, mentre
I Interjection ah!, ehi!, bravo!
N Numeral due, cento, 1990
SYM Symbol $, %, +
X Foreign word / Other marketing, online
Y Abbreviation ecc., prof.
Punctuation FF Generic Punctuation / Hyphen -, --, ...
FC Comma ,
FS Sentence End Punctuation ., !, ?
FB Brackets (, ), [, ]

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 mangia.
csubj Clausal subject Ciò che ha fatto era sbagliato.
obj Direct object Vedo la luna.
iobj Indirect object Lei mi ha dato un regalo.
ccomp Clausal complement (finite) Ha detto che era stanco.
xcomp Open clausal complement Voglio andare.
Non-Core Dependents obl Oblique nominal Si è seduto sulla sedia.
vocative Vocative Giovanni, vieni qui!
expl Expletive C'è un gatto.
dislocated Dislocated element Conosco quell'uomo.
advcl Adverbial clause modifier Sono partito dopo che è arrivato.
advmod Adverbial modifier Corri velocemente.
discourse Discourse element Beh, non sono sicuro.
aux Auxiliary Posso vedere.
cop Copula Lei è felice.
mark Subordinating marker So che lo sai.
Nominal Dependents nmod Nominal modifier La portiera della macchina.
appos Appositional modifier Sam, il mio amico.
nummod Numeric modifier Sette giorni.
acl Adjectival clause Il piano per vincere.
amod Adjectival modifier Il cielo blu.
det Determiner La fine.
case Case marking Il re di Francia.
fixed Fixed multiword expression Nonostante ciò.
flat Flat multiword name La città di New York.
compound Compound noun Cabina telefonica.
list List element Telefono, chiavi, portafoglio.
Coordination conj Conjunct Pane e burro.
cc Coordinating conjunction Pane e burro.
Special Labels aux:pass Passive auxiliary È stato rubato.
punct Punctuation Ciao!
dep Unspecified dependency (Usato per collegamenti sconosciuti)
ROOT Root of the sentence Ho pranzato.

Common Dependency Attachments (Sub-labels)
Attachment Full Name Explanation Example
:pass Passive Indicates a relationship in a passive voice construction. nsubj:pass (La finestra è stata rotta)
:nn Noun Compound Indicates that a noun is modifying another noun in a compound structure. compound:nn (Caricatore del telefono)
:prep Prepositional Refines a modifier governed specifically by a preposition. nmod:prep (Il gatto sul tappeto)
:assmod Associative Modifier Common in Romanian/Baltic languages; shows nouns modifying other nouns. nmod:assmod (La macchina di mio padre)
:poss Possessive Indicates ownership or a possessive relationship. nmod:poss (Il mio cane, il cappello di Giovanni)
:relcl Relative Clause Identifies a clause that modifies a noun phrase. acl:relcl (Il libro che ho letto)
:tmod Temporal Modifier A modifier specifically describing time or duration. nmod:tmod (Parto martedì)
:prt Particle Used for phrasal verb particles. compound:prt (Arrendersi, spegnere)
:rcomp Relative Complement Used for complements of relative clauses (common in Dutch). advcl:rcomp (L'uomo che se n'è andato)
:flat Flat Modifier Used for multi-word expressions that don't have a clear internal head. flat:name (Il 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) Italia, Roma, Francia, California
🏔️ LOC Non-political location (mountains, rivers) Oceano Pacifico, Monte Everest, Le Alpi
🏢 FAC Facility (buildings, airports, highways) Ponte del Golden Gate, Aeroporto Leonardo da Vinci, Burj Khalifa
👤 PERSON People (real or fictional) Elon Musk, Harry Potter, Alan Turing
🚩 NORP Nationalities, religious or political groups Americano, Buddista, Democratici, Giapponese
🏢 ORG Organizations (companies, institutions) Google, Nazioni Unite, Apple, FIFA
📅 DATE Absolute or relative dates 4 luglio, 2026, ieri, la prossima settimana
⌚ TIME Times smaller than a day 9:30, tramonto, dieci minuti
🎊 EVENT Named events (wars, festivals) Seconda guerra mondiale, Coachella, Giochi Olimpici
💰 MONEY Monetary values, including unit $100, 5 milioni di Euro, £50
‱ PERCENT Percentage, including "%" 20%, ottanta percento, 0,5%
⚖️ QUANTITY Measurements (weight, distance) 5km, 50kg, 30 metri quadrati
🔢 ORDINAL "First", "second", etc. primo, secondo, nono
🔢 CARDINAL Numbers not classified elsewhere 10, mille, tre
📦 PRODUCT Objects, vehicles, foods, etc. (not services) iPhone, Tesla Model S, Coca-Cola
🎨 WORK_OF_ART Titles of books, songs, etc. Monna Lisa, Bohemian Rhapsody, Amleto
📜 LAW Named legal documents La Costituzione, Trattato di Versailles
🗣️ LANGUAGE Named languages Italiano, Python, Mandarino

Esempio NLP (NLP Example)

Se elaboriamo la frase "Google ha sede in California" (Google is based in California), i livelli di analisi appaiono così:

Lemma: "Google", "avere", "sede", "in", "California"
UPOS: "PROPN(Nome proprio)", "VERB(Verbo principale)", "NOUN(Sostantivo)", "ADP(Preposizione)", "PROPN(Nome proprio)"
XPOS (ISDT): "SP", "V", "S", "E", "SP"
DEP: "Google" è il nsubj (soggetto nominale) del verbo "ha", che è la Root (radice della frase). "sede" è l' obj (oggetto diretto), e "California" è un obl (complemento obliquo) introdotto dalla preposizione "in".
NER: "Google" è un' 🏢 ORG (Organizzazione), "California" è una 🌍 GPE (Entità geopolitica).

Part-of-Speech for Main Languages

Arabic - Catalan - Chinese - Classical Chinese - Croatian - Danish - Dutch - English - Filipino - Finnish - French - German - Greek - Hebrew - Hindi - Italian - Indonesian - Japanese - Korean - Latin - Lithuanian - Macedonian - Norwegian - Polish - Portuguese - Romanian - Russian - Slovenian - Sanskrit - Spanish - Swedish - Tamil - Thai - Ukrainian - Vietnamese

  • Home
  • Translators
  • Dictionaries
  • Grammars
  • Keyboards
  • Facebook

    © Stars21 - All Rights Reserved