Indonesian UPOS XPOS 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	besar, tua, hijau, tidak dapat dipahami, pertama
	ADV	Adverb	sangat, besok, bawah, di mana, di sana
	INTJ	Interjection	sst, aduh, bravo, halo
	NOUN	Noun (common)	gadis, kucing, pohon, udara, keindahan
	PROPN	Proper Noun	Maria, Yohanes, London, NATO, HBO
	VERB	Verb	berlari, berlari, berlari, makan, makan, dimakan
Closed Class	ADP	Adposition	di, ke, selama
	AUX	Auxiliary	adalah, telah, akan, harus
	CONJ	Conjunction	dan, atau, tetapi (tag warisan)
	CCONJ	Coordinating Conjunction	dan, atau, tetapi
	SCONJ	Subordinating Conjunction	jika, sementara, bahwa
	DET	Determiner	sebuah, itu
	NUM	Numeral	1, 2017, satu, tujuh puluh tujuh, MMXIV
	PART	Particle	-nya, tidak
	PRON	Pronoun	saya, kamu, dia, dia, diri saya sendiri, diri mereka sendiri, seseorang
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.

How to read the tags: An XPOS tag like VSA translates to Verb (Position 1), Singular (Position 2), Active (Position 3). A tag like R-- translates to Preposition (Position 1), with no applicable features for Positions 2 and 3.

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Indonesian XPOS Positional Detail
Group	Position	Category	Label	Meaning
Noun	1	Lexical Class	N	Noun
	2	Number	S	Singular (e.g., buku)
	2	Number	P	Plural / Reduplication (e.g., buku-buku)
	3	Gender	D	Non-Specified (Default for most Indonesian nouns)
			M	Masculine (e.g., putra, aktor)
			F	Feminine (e.g., putri, aktris)
Verb	1	Lexical Class	V	Verb
	2	Number	S	Singular
	2	Number	P	Plural
	3	Voice	A	Active (Often prefixed with meN- / ber-)
	3	Voice	P	Passive (Often prefixed with di- / ter-)
Pronoun	1	Lexical Class	P	Personal Pronoun
	2	Number	S	Singular
	2	Number	P	Plural
	3	Person	1	1st Person (aku, saya, kami)
			2	2nd Person (kamu, kalian)
			3	3rd Person (dia, mereka)
Adjective	1	Lexical Class	A	Adjective
	2	Number	S	Singular
	2	Number	P	Plural
	3	Degree	P	Positive (baik)
	3	Degree	S	Superlative (Prefixed with ter-, e.g., terbaik)
Question	1	Lexical Class	W	Question Word
	2	Sub-Type	P	Pronoun (siapa, apa)
			D	Adverb (kapan, bagaimana)
			B	Determiner (mana)
Invariable Words (Pos 2 & 3 are always "-")	1	Lexical Class	R	Preposition / Relational (di, ke, dari)
			S	Subordinating Conjunction (bahwa, karena)
			H	Coordinating Conjunction (dan, atau)
			C	Numeral (Cardinal / Ordinal)
			D	Adverb (sangat, hanya)
			B	Determiner (ini, itu)
			M	Modal / Auxiliary (akan, telah, harus)
			T	Particle (-lah, -kah, pun)
			G	Negation (tidak, bukan, belum)
			I	Interjection (wah, oh, astaga)
			O	Copula (adalah, ialah)
			F	Foreign Word (Borrowed words not yet localized)
			Z	Punctuation
			X	Unknown / Fallback Token

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 makan.
	csubj	Clausal subject	Apa yang dia lakukan salah.
	obj	Direct object	Saya melihat bulan.
	iobj	Indirect object	Dia memberi saya hadiah.
	ccomp	Clausal complement (finite)	Dia berkata bahwa dia lelah.
	xcomp	Open clausal complement	Saya ingin pergi.
Non-Core Dependents	obl	Oblique nominal	Dia duduk di kursi.
	vocative	Vocative	Yohanes, kemari!
	expl	Expletive	Ada seekor kucing.
	dislocated	Dislocated element	Pria itu, saya mengenalnya.
	advcl	Adverbial clause modifier	Saya pergi setelah dia tiba.
	advmod	Adverbial modifier	Lari cepat.
	discourse	Discourse element	Yah, saya tidak yakin.
	aux	Auxiliary	Saya bisa melihat.
	cop	Copula	Dia bahagia.
	mark	Subordinating marker	Saya tahu bahwa kamu tahu.
Nominal Dependents	nmod	Nominal modifier	Pintu mobil.
	appos	Appositional modifier	Sam, teman saya.
	nummod	Numeric modifier	Tujuh hari.
	acl	Adjectival clause	Rencana untuk menang.
	amod	Adjectival modifier	Langit biru.
	det	Determiner	Akhir.
	case	Case marking	Raja Prancis.
	fixed	Fixed multiword expression	Meskipun demikian.
	flat	Flat multiword name	Kota New York.
	compound	Compound noun	Bilik telepon.
	list	List element	Telepon, kunci, dompet.
Coordination	conj	Conjunct	Roti dan mentega.
Coordination	cc	Coordinating conjunction	Roti dan mentega.
Special Labels	aux:pass	Passive auxiliary	Itu dicuri.
	punct	Punctuation	Halo!
	dep	Unspecified dependency	(Digunakan untuk hubungan yang tidak diketahui)
	ROOT	Root of the sentence	Saya makan siang.

Common Dependency Attachments (Sub-labels)
Attachment	Full Name	Explanation	Example
:pass	Passive	Indicates a relationship in a passive voice construction.	nsubj:pass (Jendela itu dipecahkan)
:nn	Noun Compound	Indicates that a noun is modifying another noun in a compound structure.	compound:nn (Pengisi daya telepon)
:prep	Prepositional	Refines a modifier governed specifically by a preposition.	nmod:prep (Kucing di atas keset)
:assmod	Associative Modifier	Common in Romanian/Baltic languages; shows nouns modifying other nouns.	nmod:assmod (Mobil ayah saya)
:poss	Possessive	Indicates ownership or a possessive relationship.	nmod:poss (Anjing saya, topi Yohanes)
:relcl	Relative Clause	Identifies a clause that modifies a noun phrase.	acl:relcl (Buku yang saya baca)
:tmod	Temporal Modifier	A modifier specifically describing time or duration.	nmod:tmod (Saya pergi hari Selasa)
:prt	Particle	Used for phrasal verb particles.	compound:prt (Menyerah, mematikan)
:rcomp	Relative Complement	Used for complements of relative clauses (common in Dutch).	advcl:rcomp (Pria yang pergi)
:flat	Flat Modifier	Used for multi-word expressions that don't have a clear internal head.	flat:name (Presiden 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)	Indonesia, Jakarta, Prancis, California
🏔️ LOC	Non-political location (mountains, rivers)	Samudra Pasifik, Gunung Everest, Pegunungan Alpen
🏢 FAC	Facility (buildings, airports, highways)	Jembatan Golden Gate, Bandara Soekarno-Hatta, Burj Khalifa
👤 PERSON	People (real or fictional)	Elon Musk, Harry Potter, Alan Turing
🚩 NORP	Nationalities, religious or political groups	Orang Amerika, Buddhis, Demokrat, Orang Jepang
🏢 ORG	Organizations (companies, institutions)	Google, Perserikatan Bangsa-Bangsa, Apple, FIFA
📅 DATE	Absolute or relative dates	4 Juli, 2026, kemarin, minggu depan
⌚ TIME	Times smaller than a day	9:30 pagi, matahari terbenam, sepuluh menit
🎊 EVENT	Named events (wars, festivals)	Perang Dunia II, Coachella, Olimpiade
💰 MONEY	Monetary values, including unit	$100, 5 juta Euro, £50
‱ PERCENT	Percentage, including "%"	20%, delapan puluh persen, 0.5%
⚖️ QUANTITY	Measurements (weight, distance)	5km, 50kg, 30 meter persegi
🔢 ORDINAL	"First", "second", etc.	pertama, kedua, kesembilan
🔢 CARDINAL	Numbers not classified elsewhere	10, seribu, tiga
📦 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	Konstitusi, Perjanjian Versailles
🗣️ LANGUAGE	Named languages	Bahasa Indonesia, Python, Mandarin

Universal POS, Detailed POS, NER, DEP

UPOS (Universal POS)

XPOS (Detailed POS)

Dependency

Named Entity Recognition

Contoh NLP (NLP Example)

Part-of-Speech for Main Languages