Kumparan's NLP Services
Project description
Kumparan's NLP Services
nlp-id
is a collection of modules which provides various functions for Natural Language Processing for Bahasa Indonesia. This repository contains all source code related to NLP services.
Installation
To install nlp-id
, use the following command:
$ pip install nlp-id
Data
There are some data that needs to be downloaded before using the library:
After downloading the data, you can place it inside the nlp_id/data
folder.
Usage
Description on how to use the lemmatizer, tokenizer, POS-tagger, etc. will be explained in more detail in this section.
Lemmatizer
Lemmatizer is used to get the root words from every word in a sentence.
from nlp_id.lemmatizer import Lemmatizer
lemmatizer = Lemmatizer()
lemmatizer.lemmatize('Saya sedang mencoba')
# saya sedang coba
Tokenizer
Tokenizer is used to convert text into tokens of word, punctuation, number, date, email, URL, etc. There are two kinds of tokenizer in this repository, standard tokenizer and phrase tokenizer. The standard tokenizer tokenizes the text into separate tokens where the word tokens are single-word tokens.
from nlp_id.tokenizer import Tokenizer
tokenizer = Tokenizer()
tokenizer.tokenize('Joko Widodo kembali terpilih menjadi presiden Republik Indonesia')
# ['Joko', 'Widodo', 'kembali', 'terpilih', 'menjadi', 'presiden', 'Republik', 'Indonesia']
The phrase tokenizer tokenizes the text into separate tokens where the word tokens are phrases (single or multi-word tokens).
from nlp_id.tokenizer import PhraseTokenizer
tokenizer = PhraseTokenizer()
tokenizer.tokenize('Joko Widodo kembali terpilih menjadi presiden Republik Indonesia')
# ['Joko Widodo', 'kembali', 'terpilih', 'menjadi', 'presiden', 'Republik Indonesia']
POS Tagger
POS tagger is used to obtain the Part-Of-Speech tag from a text. There are two kinds of POS tagger in this repository, standard POS tagger and phrase POS tagger. The tokens in standard POS Tagger are single-word tokens, while the tokens in phrase POS Tagger are phrases (single or multi-word tokens).
from nlp_id.postag import PosTag
postagger = PosTag()
postagger.get_pos_tag('Joko Widodo kembali terpilih menjadi presiden Republik Indonesia')
# [('Joko', 'NNP'), ('Widodo', 'NNP'), ('kembali', 'VB'), ('terpilih', 'VB'), ('menjadi', 'VB'), ('presiden', 'NN'),
('Republik', 'NNP'), ('Indonesia', 'NNP')]
postagger.get_phrase_tag('Joko Widodo kembali terpilih menjadi presiden Republik Indonesia')
# [('Joko Widodo', 'NP'), ('kembali', 'VP'), ('terpilih', 'VP'), ('menjadi', 'VP'), ('presiden', 'NN'),
('Republik Indonesia', 'NP')]
Description of tagset used for POS Tagger:
No. | Tag | Description | Example |
---|---|---|---|
1 | ADV | Adverbs. Includes adverb, modal, and auxiliary verb | sangat, hanya, justru, boleh, harus, mesti |
2 | CC | Coordinating conjunction. Coordinating conjunction links two or more syntactically equivalent parts of a sentence. Coordinating conjunction can link independent clauses, phrases, or words. | dan, tetapi, atau |
3 | DT | Determiner/article. A grammatical unit which limits the potential referent of a noun phrase, whose basic role is to mark noun phrases as either definite or indefinite. | para, sang, si |
4 | IN | Preposition. A preposition links word or phrase and constituent in front of that preposition and results prepositional phrase. | dalam, dengan, di, ke |
5 | JJ | Adjective. Adjectives are words which describe, modify, or specify some properties of the head noun of the phrase | bersih, panjang, jauh, marah |
6 | NEG | Negation | tidak, belum, jangan |
7 | NN | Noun. Nouns are words which refer to human, animal, thing, concept, or understanding | meja, kursi, monyet, perkumpulan |
8 | NNP | Proper Noun. Proper noun is a specific name of a person, thing, place, event, etc. | Indonesia, Jakarta, Piala Dunia, Idul Fitri, Jokowi |
9 | NUM | Number. Includes cardinal and ordinal number | 9876, 2019, 0,5, empat |
10 | PR | Pronoun. Includes personal pronoun and demonstrative pronoun | saya, kami, kita, kalian, ini, itu |
11 | RP | Particle. Particle which confirms interrogative, imperative, or declarative sentences | pun, lah, kah |
12 | SC | Subordinating Conjunction. Subordinating conjunction links two or more clauses and one of the clauses is a subordinate clause. | sejak, jika, seandainya, dengan, bahwa, yang |
13 | SYM | Symbols and Punctuations | +,%,@ |
14 | UH | Interjection. Interjection expresses feeling or state of mind and has no relation with other words syntactically. | ayo, mari, aduh |
15 | VB | Verb. Includes transitive verbs, intransitive verbs, active verbs, passive verbs, and copulas. | tertidur, bekerja, membaca |
16 | WH | Question words | siapa, apa, kapan, bagaimana |
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