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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     

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. Tokens that started with ku- or ended with -ku, -mu, -nya, -lah, -kah will be split if it is personal pronoun or particle.

from nlp_id.tokenizer import Tokenizer 
tokenizer = Tokenizer() 
tokenizer.tokenize('Lionel Messi pergi ke pasar di daerah Jakarta Pusat.') 
# ['Lionel', 'Messi', 'pergi', 'ke', 'pasar', 'di', 'daerah', 'Jakarta', 'Pusat', '.']

tokenizer.tokenize('Lionel Messi pergi ke rumahmu di daerah Jakarta Pusat.') 
# ['Lionel', 'Messi', 'pergi', 'ke', 'rumah', 'mu', 'di', 'daerah', 'Jakarta', 'Pusat', '.']

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('Lionel Messi pergi ke pasar di daerah Jakarta Pusat.') 
# ['Lionel Messi', 'pergi', 'ke', 'pasar', 'di', 'daerah', 'Jakarta Pusat', '.']

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('Lionel Messi pergi ke pasar di daerah Jakarta Pusat.') 
# [('Lionel', 'NNP'), ('Messi', 'NNP'), ('pergi', 'VB'), ('ke', 'IN'), ('pasar', 'NN'), ('di', 'IN'), ('daerah', 'NN'),  
  ('Jakarta', 'NNP'), ('Pusat', 'NNP'), ('.', 'SYM')]

postagger.get_phrase_tag('Lionel Messi pergi ke pasar di daerah Jakarta Pusat.') 
# [('Lionel Messi', 'NP'), ('pergi', 'VP'), ('ke', 'IN'), ('pasar', 'NN'), ('di', 'IN'), ('daerah', 'NN'), 
  ('Jakarta Pusat', 'NP'), ('.', 'SYM')]

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, ini, itu, nya
4 FW Foreign word. Foreign word is a word which comes from foreign language and is not yet included in Indonesian dictionary workshop, business, e-commerce
5 IN Preposition. A preposition links word or phrase and constituent in front of that preposition and results prepositional phrase. dalam, dengan, di, ke
6 JJ Adjective. Adjectives are words which describe, modify, or specify some properties of the head noun of the phrase bersih, panjang, jauh, marah
7 NEG Negation tidak, belum, jangan
8 NN Noun. Nouns are words which refer to human, animal, thing, concept, or understanding meja, kursi, monyet, perkumpulan
9 NNP Proper Noun. Proper noun is a specific name of a person, thing, place, event, etc. Indonesia, Jakarta, Piala Dunia, Idul Fitri, Jokowi
10 NUM Number. Includes cardinal and ordinal number 9876, 2019, 0,5, empat
11 PR Pronoun. Includes personal pronoun and demonstrative pronoun saya, kami, kita, kalian, ini, itu, nya, yang
12 RP Particle. Particle which confirms interrogative, imperative, or declarative sentences pun, lah, kah
13 SC Subordinating Conjunction. Subordinating conjunction links two or more clauses and one of the clauses is a subordinate clause. sejak, jika, seandainya, dengan, bahwa
14 SYM Symbols and Punctuations +,%,@
15 UH Interjection. Interjection expresses feeling or state of mind and has no relation with other words syntactically. ayo, nah, ah
16 VB Verb. Includes transitive verbs, intransitive verbs, active verbs, passive verbs, and copulas. tertidur, bekerja, membaca
17 ADJP Adjective Phrase. A group of words headed by an adjective that describes a noun or a pronoun sangat tinggi
18 DP Date Phrase. Date written with whitespaces 1 Januari 2020
19 NP Noun Phrase. A phrase that has a noun (or indefinite pronoun) as its head Jakarta Pusat, Lionel Messi
20 NUMP Number Phrase. 10 juta
21 VP Verb Phrase. A syntactic unit composed of at least one verb and its dependents tidak makan

Stopword

nlp-id also provide list of Indonesian stopword.

from nlp_id.stopword import StopWord 
stopword = StopWord() 
stopword.get_stopword() 
# [{list_of_nlp_id_stopword}]    

Stopword Removal is used to remove every Indonesian stopword from the given text.

from nlp_id.stopword import StopWord 
text = "Lionel Messi pergi Ke pasar di area Jakarta Pusat" # single sentence
stopword = StopWord() 
stopword.remove_stopword(text)
# Lionel Messi pergi pasar area Jakarta Pusat  

paragraph = "Lionel Messi pergi Ke pasar di area Jakarta Pusat itu. Sedangkan Cristiano Ronaldo ke pasar Di area Jakarta Selatan. Dan mereka tidak bertemu begini-begitu."
stopword.remove_stopword(text)
# Lionel Messi pergi pasar area Jakarta Pusat. Cristiano Ronaldo pasar area Jakarta Selatan. bertemu.

Training and Evaluation

Our model is trained using stories from kumparan as the dataset. We managed to get ~93% accuracy on our test set.

Citation

DOI

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