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 groot, oud, groen, onbegrijpelijk, eerste
ADV Adverb erg, morgen, beneden, waar, daar
INTJ Interjection sst, au, bravo, hallo
NOUN Noun (common) meisje, kat, boom, lucht, schoonheid
PROPN Proper Noun Maria, Johannes, Londen, NAVO, HBO
VERB Verb rennen, rent, rennend, eten, at, gegeten
Closed Class ADP Adposition in, naar, tijdens
AUX Auxiliary is, heeft (gedaan), zal (doen), zou (moeten doen)
CONJ Conjunction en, of, maar (legacy-tag)
CCONJ Coordinating Conjunction en, of, maar
SCONJ Subordinating Conjunction als, terwijl, dat
DET Determiner een, de, het
NUM Numeral 1, 2017, een, zevenenzeventig, MMXIV
PART Particle 's (genitief), niet
PRON Pronoun ik, jij, hij, zij, mezelf, zichzelf, iemand
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.

Dutch XPOS Anatomy: Dutch XPOS tags are based on the CGN (Corpus Gesproken Nederlands) tagset. They are composed of a Primary Tag (the word category) followed by Morphological Features (specific grammatical attributes) often enclosed in parentheses or separated by pipes.
Structure: Category | Type | Tense/Degree | Number/Gender
Example: WW(pv,tgw,ev)
English: Verb (Finite form, Present tense, Singular)
Dutch: Werkwoord (Persoonsvorm, Tegenwoordige tijd, Enkelvoud)

Try our Dutch XPOS tagging now.

Dutch xpos tags
Category Tag Meaning Dutch Term
Nouns N Noun (General) Zelfstandig naamwoord
N(soort) Common noun Soortnaam
N(eigen) Proper noun Eigennaam
Verbs WW Verb (General) Werkwoord
WW(pv) Finite verb Persoonsvorm
WW(inf) Infinitive Infinitief
WW(vd) Past participle Voltooid deelwoord
WW(od) Present participle Onvoltooid deelwoord
Modifiers ADJ Adjective Bijvoeglijk naamwoord
BW Adverb Bijwoord
Pronouns & Articles VNW Pronoun Voornaamwoord
LID Article / Determiner Lidwoord
Adpositions VZ Preposition / Adposition Voorzetsel
VZ(init) Preposition (initial) Voorzetsel
VZ(fin) Postposition (final) Achterzetsel
Conjunctions VG Conjunction (General) Voegwoord
VG(neven) Coordinating conjunction Nevenschikkend
VG(onder) Subordinating conjunction Onderschikkend
Numerals TW Numeral (General) Telwoord
TW(hoofd) Cardinal numeral Hoofdtelwoord
TW(rang) Ordinal numeral Rangtelwoord
Special & Others TSW Interjection Tussenwerpsel
SPEC(deeleigen) Part of a proper name Deeleigennaam
SPEC(vreemd) Foreign word Vreemd
SPEC(symb) Symbol Symbool
SPEC(afgebr) Truncated word Afgebroken
Punctuation LET Punctuation Leesteken

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 eet.
csubj Clausal subject Wat hij deed was fout.
obj Direct object Ik zie de maan.
iobj Indirect object Ze gaf mij een cadeau.
ccomp Clausal complement (finite) Hij zei dat hij moe was.
xcomp Open clausal complement Ik wil gaan.
Non-Core Dependents obl Oblique nominal Hij zat op de stoel.
vocative Vocative Johannes, kom hier!
expl Expletive Er is een kat.
dislocated Dislocated element Die man, ik ken hem.
advcl Adverbial clause modifier Ik vertrok nadat hij aankwam.
advmod Adverbial modifier Ren snel.
discourse Discourse element Nou, ik weet het niet zeker.
aux Auxiliary Ik kan zien.
cop Copula Ze is blij.
mark Subordinating marker Ik weet dat je het weet.
Nominal Dependents nmod Nominal modifier De deur van de auto.
appos Appositional modifier Sam, mijn vriend.
nummod Numeric modifier Zeven dagen.
acl Adjectival clause Het plan om te winnen.
amod Adjectival modifier De blauwe lucht.
det Determiner Het einde.
case Case marking De koning van Frankrijk.
fixed Fixed multiword expression Ondanks dat.
flat Flat multiword name De stad Amsterdam.
compound Compound noun Telefooncel.
list List element Telefoon, sleutels, portemonnee.
Coordination conj Conjunct Brood en boter.
cc Coordinating conjunction Brood en boter.
Special Labels aux:pass Passive auxiliary Het werd gestolen.
punct Punctuation Hallo!
dep Unspecified dependency (Gebruikt voor onbekende verbindingen)
ROOT Root of the sentence Ik at de lunch.

Common Dependency Attachments (Sub-labels)
Attachment Full Name Explanation Example
:pass Passive Indicates a relationship in a passive voice construction. nsubj:pass (Het raam was gebroken)
:nn Noun Compound Indicates that a noun is modifying another noun in a compound structure. compound:nn (Telefoonoplader)
:prep Prepositional Refines a modifier governed specifically by a preposition. nmod:prep (De kat op de mat)
:assmod Associative Modifier Common in Romanian/Baltic languages; shows nouns modifying other nouns. nmod:assmod (De auto van mijn vader)
:poss Possessive Indicates ownership or a possessive relationship. nmod:poss (Mijn hond, Johannes' hoed)
:relcl Relative Clause Identifies a clause that modifies a noun phrase. acl:relcl (Het boek dat ik las)
:tmod Temporal Modifier A modifier specifically describing time or duration. nmod:tmod (Ik vertrek dinsdag)
:prt Particle Used for phrasal verb particles. compound:prt (Geef op, sluit af)
:rcomp Relative Complement Used for complements of relative clauses (common in Dutch). advcl:rcomp (De man die vertrok)
:flat Flat Modifier Used for multi-word expressions that don't have a clear internal head. flat:name (President 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) Nederland, Amsterdam, Frankrijk, Californië
🏔️ LOC Non-political location (mountains, rivers) Stille Oceaan, Mount Everest, De Alpen
🏢 FAC Facility (buildings, airports, highways) Erasmusbrug, Schiphol, Burj Khalifa
👤 PERSON People (real or fictional) Willem-Alexander, Harry Potter, Alan Turing
🚩 NORP Nationalities, religious or political groups Nederlander, boeddhist, democraten, Japanner
🏢 ORG Organizations (companies, institutions) Google, Verenigde Naties, Apple, FIFA
📅 DATE Absolute or relative dates 4 juli, 2026, gisteren, volgende week
⌚ TIME Times smaller than a day 9:30 uur, zonsondergang, tien minuten
🎊 EVENT Named events (wars, festivals) Tweede Wereldoorlog, Pinkpop, Olympische Spelen
💰 MONEY Monetary values, including unit $100, 5 miljoen euro, £50
‱ PERCENT Percentage, including "%" 20%, tachtig procent, 0,5%
⚖️ QUANTITY Measurements (weight, distance) 5 km, 50 kg, 30 vierkante meter
🔢 ORDINAL "First", "second", etc. eerste, 2e, negende
🔢 CARDINAL Numbers not classified elsewhere 10, duizend, drie
📦 PRODUCT Objects, vehicles, foods, etc. (not services) iPhone, Tesla Model S, Coca-Cola
🎨 WORK_OF_ART Titles of books, songs, etc. Mona Lisa, Bohemian Rhapsody, Hamlet
📜 LAW Named legal documents De Grondwet, Verdrag van Versailles
🗣️ LANGUAGE Named languages Nederlands, Python, Mandarijn

Voorbeeld van natuurlijke taalverwerking (NLP Example)

Als we de zin „Google is gevestigd in Amsterdam“ (Google is based in Amsterdam) verwerken, zien de lagen er als volgt uit:

Lemma (Llemma): "Google", "zijn", "gevestigd", "in", "Amsterdam"
UPOS (Universele woordsoorttags): "PROPN(Eigennaam)", "AUX(Hulpwerkwoord)", "VERB(Hoofdwerkwoord)", "ADP(Voorzetsel)", "PROPN(Eigennaam)"
XPOS (Specifieke CGN-woordsoorttags): "N(eigen,ev,neut)", "WW(pv,tgw,ev)", "WW(vd,vrij,zonder)", "VZ(init)", "N(eigen,ev,neut)"
DEP (Syntactische afhankelijkheden): „Google“ is het nsubj:pass (passief nominal subject) van de passieve constructie. „is“ is het aux:pass (passief hulpwerkwoord). „gevestigd“ is de Root (hoofdwerkwoord/sentence root). „Amsterdam“ is het obl (adverbiale bepaling) verbonden door het voorzetsel „in“.
NER (Herkenning van benoemde entiteiten): „Google“ is een 🏢 ORG (Organisatie), en „Amsterdam“ is een 🌍 GPE (Geopolitieke entiteit).

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