Syntactic Analyzer

One of the analyzes to help understanding the meaning of a text is to perform a syntactic analysis that can be done with the Syntactic Analyzer module. In the process, the text will be broken down into smaller parts and given certain labels. The Syntactic Analyzer module performs three analyzes carried out by the components: Tokenizer, Stemmer, and Part-of-Speech tagger (POS tagger).

This module is used by entering text either in the form of paragraphs or sentences and returning the results of the analysis.

Tokenizer

The Tokenizer is a component in the Syntactic Analyzer module that serves to cut text into a set of tokens (words) and/or sentences. The process of cutting the word is then called the term tokenization. Tokenization is the initial process of language processing. This Tokenizer is able to analyze data text with the following conditions:

• Handles the special cases contained in the General Guidelines for Indonesian Spelling (PUEBI), such as writing money (Rp50), writing titles (Dr.), and separating two or more different words not separated by spaces (Jokowi-JK, Anyer-Panarukan).
• Supports various types of text formats such as news or social media.

Illustration

• Tokenize paragraph into sentences

• Tokenize sentence into words

Stemmer

The text analysis needed when understanding the meaning of a sentence is the identification of the basic word because words that have the same basic word will have the same meaning.

• Change passive sentences becomes active through changing their affixes. Example: "di" becomes "me" on "dibunuh" becomes "membunuh".
• Increase the relevance of search engine results on a site by matching search keywords that have been processed by the Stemmer API.

Illustration

Part-of-Speech Tagger

In Indonesian, each word occupies a specific class of words that have different functions in a sentence structure. Word classes are needed to help process language automatically at a higher level, such as knowing the structure and meaning in a sentence. POS Tagger is used to obtain various types of word classes, including nouns, pronouns, verbs, adjectives, prepositions, numbers, adverbs, punctuation, and so on. The output from this module is in the form of a word class for each word contained in the input text, either in the form of articles or sentences.

Illustration

Request Method

POST

Request URL

https://api.prosa.ai/v2/text/basic-nlp

Request Header

Key	Data Type	Description	Value
Content-Type	string	Media type of the body sent to the API. Only Support 'application/json'	application/json
x-api-key	string	API Key Acquired from Prosa API Console	[YOUR_API_KEY]

Request Body

The request body accepts the following parameter(s) in JSON format.

Parameter	Data Type	Description	Auto	Required
text	string	text to be processed		True
granularity	enum	part-of-speech types granularity	'fine_grained'	False

Granularity Enums

Part-of-speech tags granularity.

Granularity	Description
coarse	16 classes based on universal dependencies pos tags.
fine_graned	28 classed based on INACL (Indonesia Association of Computational Linguistics) convention.

Example

Sample Request (JSON)

{
    "text":"Aku mencintai bahasa Indonesia karena aku orang Indonesia."
}

Sample Response (JSON)

{
    "sentences": [
        {
            "sentence": "Aku mencintai bahasa Indonesia karena aku orang Indonesia.",
            "begin_offset": 0,
            "length": 58,
            "lemmatized_length": 54,
            "lemmatized_sentence": "aku cinta bahasa indonesia karena aku orang indonesia.",
            "tokens": [
                {
                    "token": "Aku",
                    "length": 3,
                    "pos_tag": "PRN",
                    "begin_offset": 0,
                    "lemma": "aku",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 0
                },
                {
                    "token": "mencintai",
                    "length": 9,
                    "pos_tag": "VBT",
                    "begin_offset": 4,
                    "lemma": "cinta",
                    "affixes": {
                        "derivational_prefix": [
                            "me"
                        ],
                        "derivational_suffix": "i",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 4
                },
                {
                    "token": "bahasa",
                    "length": 6,
                    "pos_tag": "NNO",
                    "begin_offset": 14,
                    "lemma": "bahasa",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 10
                },
                {
                    "token": "Indonesia",
                    "length": 9,
                    "pos_tag": "NNP",
                    "begin_offset": 21,
                    "lemma": "indonesia",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 17
                },
                {
                    "token": "karena",
                    "length": 6,
                    "pos_tag": "CSN",
                    "begin_offset": 31,
                    "lemma": "karena",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 27
                },
                {
                    "token": "aku",
                    "length": 3,
                    "pos_tag": "PRN",
                    "begin_offset": 38,
                    "lemma": "aku",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 34
                },
                {
                    "token": "orang",
                    "length": 5,
                    "pos_tag": "NNO",
                    "begin_offset": 42,
                    "lemma": "orang",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 38
                },
                {
                    "token": "Indonesia",
                    "length": 9,
                    "pos_tag": "NNP",
                    "begin_offset": 48,
                    "lemma": "indonesia",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 44
                },
                {
                    "token": ".",
                    "length": 1,
                    "pos_tag": "SYM",
                    "begin_offset": 57,
                    "lemma": ".",
                    "affixes": {
                        "derivational_prefix": [],
                        "derivational_suffix": "",
                        "particle_suffix": "",
                        "possesive_suffix": ""
                    },
                    "lemmatized_begin_offset": 53
                }
            ],
            "length": 58,
            "begin_offset": 0
        }
    ]
}

Fine Grained POS Tags

Based on INACL (Indonesia Association of Computational Linguistics) convention.

Name	Code	Example	Description
Adjective	ADJ	biru, sakit, gelisah, cerdas, cantik, panjang, ordinal non-kuantitatif: pertama, kedua, ketiga (example: juara pertama, abad ke-17)	Adjectives are words that typically modify nouns and specify their properties or attributes
Adverb	ADV	sangat, enggan, harus, mesti, agak, hanya, semakin	Adverbs are words that typically modify verbs for such categories as time, place, direction or manner.
Articles	ART	para, si, sang, hang, ini, itu	Articles are words that modify nouns or noun phrases and express the indefinite reference of the noun phrase in context.
Coordinative Conjunction	CCN	dan, atau, tetapi	A coordinating conjunction is a word that links words or larger constituents without syntactically subordinating one to the other and expresses a semantic relationship between them.
Subordinative Conjunction	CSN	jika, meskipun, walaupun	A subordinating conjunction is a conjunction that links constructions by making one of them a constituent of the other.
Interjection	INT	aduh, astaga, wah, aduhai, nah, astaga	An interjection is a word that is used most often as an exclamation or part of an exclamation.
Quantifiers	KUA	sesuatu, semua, beberapa, berbagai, sebagian, segenap, masing-masing, segala	Quantifiers are words that modify nouns or noun phrases and express the definite reference of the noun phrase in context.
Negation	NEG	tidak, bukan, tak, enggak	Negation word are words used to express the contradiction or denial of something
Noun	NNO	buku, mobil, malaikat, pikiran	Nouns are a part of speech typically denoting a person, place, thing, animal or idea.
Proper Noun	NNP	Jakarta, Indonesia, Burhan Silalahi	A proper noun is a noun (or nominal content word) that is the name (or part of the name) of a specific individual, place, or object.
Numeral	NUM	satu, dua, sebuah, pertama	A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction.
Preposition	PPO	di, ke, dari, tentang	Adposition is a cover term for prepositions and postpositions.
Interrogative Pronoun	PRI	apa, siapa, bagaimana	Interrogative Pronouns is pronouns used to replace something that is a question in a question sentence.
Cliticized Pronoun	PRK	mu, ku, nya	Cliticized Pronoun is a pronoun that is word morpheme
Pronoun	PRN	saya, anda, kamu, sesuatu, seseorang, itu, ini	Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context.
Relative Pronoun	PRR	yang, tempat	Relative Pronouns are words that can be used to replace the main part and/or link it with it’s explanation.
Particle	PAR	pun, per, lah, toh, kah	Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech (e.g. adpositions, coordinating conjunctions, subordinating conjunctions or auxiliary verbs).
Punctuation	PUN	., ?, !, ", /, -, ;	a sign used to separate various parts of a written language unit (words, phrases, and sentences) and in some cases can affect the meaning of the language unit.
Character Symbols	SYM	Rp, $, Ω, °	A symbol is a word-like entity that differs from ordinary words by form, function, or both.
Tense, Aspect, Modality, Evidentiality	TAME	telah, akan, bakal, sudah, sedang, lagi, masih, pernah, ingin, harus, perlu, boleh, pasti, tampaknya	a group of words that change the context of the tense, aspect, modality, or evidentiality of a predicate.
Intransitive Verb	VBI	duduk, menangis, bergembira, percaya	An intransitive verb does not have an object.
Transitive Verb	VBT	membaca, menyirami, membelikan	A transitive verb is one that is used with an object: a noun, phrase, or pronoun that refers to the person or thing that is affected by the action of the verb.
Linking Verb	VBL	adalah, ialah, merupakan, menjadi	An auxiliary is a function word that accompanies the lexical verb of a verb phrase and expresses grammatical distinctions not carried by the lexical verb, such as person, number, tense, mood, aspect, voice or evidentiality.
Passive Verb	VBP	dipukul, dipenuhi, disembuhkan	Passive Verb is a verb where it’s subject is the patient.

Coarse POS Tags

Based on universal dependencies pos tags.

Name	Code	Example	Description
Adjective	ADJ	biru, sakit, gelisah, cerdas	Adjectives are words that typically modify nouns and specify their properties or attributes.
Adposition	ADP	di, ke, dari, tentang, untuk	Adposition is a cover term for prepositions and postpositions.
Adverb	ADV	enggan, agaknya, masih	Adverbs are words that typically modify verbs for such categories as time, place, direction or manner.
Coordinating Conjunction	CCONJ	dan, atau, tetapi, baik	A coodinating conjunction is a word that links words or larger constituents without syntactically subordinating one to other and expresses a semantic relationship between them
Determiner	DET	sesuatu, semua, beberapa	Determiners are words that modify nouns or noun phrases and express the reference of the noun phrase in context.
Interjection	INTJ	aduh, astaga, wah, maaf	An interjection is a word that is used most often as an exclamation or part of an exclamation
Noun	NOUN	buku, mobil, malaikat, pikiran	Nouns are a part of speech typically denoting a person, place, thing, animal or idea.
Numeral	NUM	satu, dua, sebuah, kedua	A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction.
Particle	PART	pun, per	Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech (e.g. adpositions, coordinating conjunctions, subordinating conjunctions or auxiliary verbs)
Pronoun	PRON	saya, anda, seseorang	Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context.
Proper Noun	PROPN	Jakarta, Indonesia, Burhan Silalahi	A proper noun is a noun (or nominal content word) that is the name (or part of the name) of a specific individual, place, or object.
Subordinating conjunction	SCONJ	jika, sejak, meskipun	A subordinating conjunction is a conjunction that links constructions by making one of them a constituent of the other.
Symbol	SYM	? . ! -	A symbol is a word-like entity that differs from ordinary words by punctuation, form, function, or both.
Verb	VERB	duduk, membaca, dipenuhi, ada	A verb is a member of the syntactic class of words that typically signal events and actions, can constitute a minimal predicate in a clause, and govern the number and types of other constituents which may occur in the clause

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Version History

Below is the version history of our Syntactic Analyzer API.

Version	F1	Test Data
1.0	96.13%	10,890 token

Questions?

We do our best to make this documentation clear and user friendly, but if you have unanswered questions, please send email to support@prosa.ai.