Awesome

NLP Bahasa Indonesia Resources

This repository provides link to useful dataset and another resources for NLP in Bahasa Indonesia.

Last Update: 15 Mar 2022

Table of contents

• Corpus
  • Named Entity Recognition
  • POS-Tagging
  • Question and Answering
  • Paraphrasing
  • Text Summarization
  • Hate-speech
  • Word Analogy
  • Formal-Informal
  • Multilingual Parallel
  • Unsupervised Corpus
  • Voice-Text
  • Puisi and Pantun
• Dictionary
  • Synonym
  • Sentiment
  • Position or Degree
  • Root Words
  • Slang Words
  • Stop Words
  • Swear Words
  • Composite Words
  • Number Words
  • Calendar Words
  • Emoticon
  • Acronym
  • Indonesia Region
  • Country
  • Region
  • Title of Name
  • Gender by Name
  • Organization
• Articles and Papers
  • POS-Tagging
  • Word Embedding
  • Topic Analysis
  • Text Classification
• Pre-trained Models
• Usable Library
• Spelling Correction
• Twitter Scraping
• Other Resources

Corpus

Named Entity Recognition

1. Product NER. https://github.com/dziem/proner-labeled-text
2. NER-grit. https://github.com/grit-id/nergrit-corpus

POS-Tagging

1. IDN Tagged Corpus. https://github.com/famrashel/idn-tagged-corpus
2. Indonesian Part-of-Speech (POS) Tagging. https://github.com/kmkurn/id-pos-tagging/blob/master/data/dataset.tar.gz

Question and Answering

1. TydiQA. https://github.com/google-research-datasets/tydiqa

Paraphrasing

1. Quora Paraphrasing. https://github.com/louisowen6/quora_paraphrasing_id
2. Paraphrase Adversaries from Word Scrambling. https://github.com/Wikidepia/indonesian_datasets/tree/master/paraphrase/paws

Text Summarization

1. Indosum. https://github.com/kata-ai/indosum
2. Liputan6. https://huggingface.co/datasets/id_liputan6

Hate-speech

1. ID Multi Label Hate Speech. https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection

Word Analogy

1. KAWAT. https://github.com/kata-ai/kawat

Formal-Informal

1. STIF-Indonesia. https://github.com/haryoa/stif-indonesia
2. IndoCollex. https://github.com/haryoa/indo-collex
3. https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection/blob/master/new_kamusalay.csv

Multilingual Parallel

1. https://huggingface.co/datasets/alt
2. https://opus.nlpl.eu/bible-uedin.php
3. http://www.statmt.org/cc-aligned/
4. https://huggingface.co/datasets/id_panl_bppt
5. https://huggingface.co/datasets/open_subtitles
6. https://huggingface.co/datasets/opus100
7. https://huggingface.co/datasets/tapaco
8. https://huggingface.co/datasets/wiki_lingua

Unsupervised Corpus

1. OSCAR. https://oscar-corpus.com/
2. Online Newspaper. https://github.com/feryandi/Dataset-Artikel
3. IndoNLU. https://huggingface.co/datasets/indonlu
4. IndoNLG. https://github.com/indobenchmark/indonlg
5. IndoNLI. https://github.com/ir-nlp-csui/indonli
6. IndoBERTweet. https://github.com/indolem/IndoBERTweet
7. http://data.statmt.org/cc-100/
8. https://huggingface.co/datasets/id_clickbait
9. https://huggingface.co/datasets/id_newspapers_2018
10. https://opus.nlpl.eu/QED.php

Voice-Text

1. https://huggingface.co/datasets/common_voice
2. https://huggingface.co/datasets/covost2

Puisi and Pantun

1. https://github.com/ilhamfp/puisi-pantun-generator

Dictionary

Synonym

1. https://github.com/victoriasovereigne/tesaurus

Sentiment

1. (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negatif_ta2.txt
2. (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negative_add.txt
3. (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negative_keyword.txt
4. (Negative) https://github.com/masdevid/ID-OpinionWords/blob/master/negative.txt
5. (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positif_ta2.txt
6. (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positive_add.txt
7. (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positive_keyword.txt
8. (Positive) https://github.com/masdevid/ID-OpinionWords/blob/master/positive.txt
9. (Score) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/sentimentword.txt
10. (InSet Lexicon) https://github.com/fajri91/InSet [Paper]
11. (Twitter Labelled Sentiment) https://www.researchgate.net/profile/Ridi_Ferdiana/publication/339936724_Indonesian_Sentiment_Twitter_Dataset/data/5e6d64c6a6fdccf994ca18aa/Indonesian-Sentiment-Twitter-Dataset.zip?origin=publicationDetail_linkedData [Paper]
12. https://huggingface.co/datasets/senti_lex

Position or Degree

1. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/psuf.txt
2. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lldr.txt
3. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/opos.txt
4. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ptit.txt

Root Words

1. https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/rootword.txt
2. https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.original.txt
3. https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.txt
4. https://github.com/prasastoadi/serangkai/blob/master/serangkai/kamus/data/kamus-kata-dasar.csv

I have made the combined root words list from all of the above repositories.

Slang Words

1. https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/kbba.txt
2. https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/slangword.txt
3. https://github.com/panggi/pujangga/blob/master/resource/formalization/formalizationDict.txt

I have made the combined slang words dictionary from all of the above repositories.

Stop Words

1. https://github.com/yasirutomo/python-sentianalysis-id/blob/master/data/feature_list/stopwordsID.txt
2. https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/stopword.txt
3. https://github.com/abhimantramb/elang/tree/master/word2vec/utils/stopwords-list

I have made the combined stop words list from all of the above repositories.

Swear Words

1. https://github.com/abhimantramb/elang/blob/master/word2vec/utils/swear-words.txt

Composite Words

1. https://github.com/panggi/pujangga/blob/master/resource/tokenizer/compositewords.txt

Number Words

1. https://github.com/panggi/pujangga/blob/master/resource/netagger/morphologicalfeature/number.txt

Calendar Words

1. https://github.com/onlyphantom/elang/blob/master/build/lib/elang/word2vec/utils/negative/calendar-words.txt

Emoticon

1. https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/emoticon.txt
2. https://github.com/jolicode/emoji-search/blob/master/synonyms/cldr-emoji-annotation-synonyms-id.txt
3. https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/emoticon.txt

Acronym

1. https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/acronym.txt
2. https://github.com/panggi/pujangga/blob/master/resource/sentencedetector/acronym.txt
3. https://id.wiktionary.org/wiki/Lampiran:Daftar_singkatan_dan_akronim_dalam_bahasa_Indonesia#A

Indonesia Region

1. https://github.com/abhimantramb/elang/blob/master/word2vec/utils/indonesian-region.txt
2. https://github.com/edwardsamuel/Wilayah-Administratif-Indonesia/tree/master/csv
3. https://github.com/pentagonal/Indonesia-Postal-Code/tree/master/Csv

Country

1. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/country.txt

Region

1. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lpre.txt

Title of Name

1. https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ppre.txt

Gender by Name

1. https://github.com/seuriously/genderprediction/blob/master/namatraining.txt

Organization

1. https://github.com/panggi/pujangga/blob/master/resource/reference/opre.txt

Articles and Papers

POS-Tagging

1. https://medium.com/@puspitakaban/pos-tagging-bahasa-indonesia-dengan-flair-nlp-c12e45542860
2. Manually Tagged Indonesian Corpus [Paper] [GitHub]

Word Embedding

1. (FastText). https://structilmy.com/2019/08/membuat-model-word-embedding-fasttext-bahasa-indonesia/
2. (Word2Vec). https://yudiwbs.wordpress.com/2018/03/31/word2vec-wikipedia-bahasa-indonesia-dengan-python-gensim/

Topic Analysis

1. (Introduction to LSA & LDA). https://monkeylearn.com/blog/introduction-to-topic-modeling/
2. (Introduction to LDA w/ Code & Tips). https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/
3. (Topic Modeling Methods Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper_21-A_Survey_of_Topic_Modeling_in_Text_Mining.pdf
4. (Original LDA Paper). http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
5. (LDA Python Library). https://pypi.org/project/lda/; https://radimrehurek.com/gensim/models/ldamodel.html; https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
6. (Original CTM Paper). http://people.ee.duke.edu/~lcarin/Blei2005CTM.pdf
7. (CTM Python Library). https://pypi.org/project/tomotopy/; https://github.com/kzhai/PyCTM
8. (Gaussian LDA Paper). https://www.aclweb.org/anthology/P15-1077.pdf
9. (Gaussian LDA Library). https://github.com/rajarshd/Gaussian_LDA
10. (Temporal Topic Modeling Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper_21-A_Survey_of_Topic_Modeling_in_Text_Mining.pdf
11. (TOT: A Non-Markov Continuous-Time Model of Topical Trends Paper). https://people.cs.umass.edu/~mccallum/papers/tot-kdd06s.pdf
12. (TOT Library). https://github.com/ahmaurya/topics_over_time
13. (Example of LDA in Bahasa Project Code). https://github.com/kirralabs/text-clustering

Text Classification

Zero-shot Learning

1. (Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach) https://arxiv.org/pdf/1909.00161.pdf | https://github.com/yinwenpeng/BenchmarkingZeroShot
2. (Integrating Semantic Knowledge to Tackle Zero-shot Text Classification) https://arxiv.org/abs/1903.12626 | https://github.com/JingqingZ/KG4ZeroShotText
3. (Train Once, Test Anywhere: Zero-Shot Learning for Text Classification) https://arxiv.org/abs/1712.05972 | https://amitness.com/2020/05/zero-shot-text-classification/
4. (Zero-shot Text Classification With Generative Language Models) https://arxiv.org/abs/1912.10165 | https://amitness.com/2020/06/zero-shot-classification-via-generation/
5. (Zero-shot User Intent Detection via Capsule Neural Networks) https://arxiv.org/abs/1809.00385 | https://github.com/congyingxia/ZeroShotCapsule

Few-shot Learning

1. (Few-shot Text Classification with Distributional Signatures) https://arxiv.org/pdf/1908.06039.pdf | https://github.com/YujiaBao/Distributional-Signatures
2. (Few Shot Text Classification with a Human in the Loop) https://katbailey.github.io/talks/Few-shot%20text%20classification.pdf | https://github.com/katbailey/few-shot-text-classification
3. (Induction Networks for Few-Shot Text Classification) https://arxiv.org/pdf/1902.10482v2.pdf | https://github.com/zhongyuchen/few-shot-learning

Pre-trained Models

1. Indo-BERT. https://github.com/indobenchmark/indonlu & https://huggingface.co/indobenchmark/indobert-base-p1
2. Indo-BERTweet. https://github.com/indolem/IndoBERTweet & https://huggingface.co/indolem/indobertweet-base-uncased
3. Transformer-based Pre-trained Model in Bahasa. https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers
4. Generate Word-Embedding / Sentence-Embedding using pre-Trained Multilingual Bert model. (https://colab.research.google.com/drive/1yFphU6PW9Uo6lmDly_ud9a6c4RCYlwdX#scrollTo=Zn0n2S-FWZih). P.S: Just change the model using 'bert-base-multilingual-uncased'
5. https://github.com/meisaputri21/Indonesian-Twitter-Emotion-Dataset. [Paper]
6. https://github.com/Kyubyong/wordvectors
7. https://drive.google.com/uc?id=0B5YTktu2dOKKNUY1OWJORlZTcUU&export=download
8. https://github.com/deryrahman/word2vec-bahasa-indonesia
9. https://sites.google.com/site/rmyeid/projects/polyglot

Usable Library

1. Pujangga: Indonesian Natural Language Processing REST API. https://github.com/panggi/pujangga
2. Sastrawi Stemmer Bahasa Indonesia. https://github.com/sastrawi/sastrawi
3. NLP-ID. https://github.com/kumparan/nlp-id
4. MorphInd: Indonesian Morphological Analyzer. http://septinalarasati.com/morphind/
5. INDRA: Indonesian Resource Grammar. https://github.com/davidmoeljadi/INDRA
6. Typo Checker. https://github.com/mamat-rahmat/checker_id
7. Multilingual NLP Package. https://github.com/flairNLP/flair
8. spaCy [GitHub] [Tutorial]
9. https://github.com/yohanesgultom/nlp-experiments
10. https://github.com/yasirutomo/python-sentianalysis-id
11. https://github.com/riochr17/Analisis-Sentimen-ID
12. https://github.com/yusufsyaifudin/indonesia-ner

Spelling Correction

You can adjust this code with Bahasa corpus to do the spelling correction

Twitter Scraping

1. GetOldTweets3. https://github.com/Mottl/GetOldTweets3

Usage:

import GetOldTweets3 as got
tweetCriteria=got.manager.TweetCriteria().setQuerySearch('#CoronaVirusIndonesia').setSince("2020-01-01").setUntil("2020-03-05").setNear("Jakarta, Indonesia").setLang("id")
tweets=got.manager.TweetManager.getTweets(tweetCriteria)
for tweet in tweets:
	print(tweet.username)
	print(tweet.text)
	print(tweet.date)
	print("tweet.to")
	print("tweet.retweets")
	print("tweet.favorites")
	print("tweet.mentions")
	print("tweet.hashtags")
	print("tweet.geo")

2. Tweepy. http://docs.tweepy.org/en/latest/

Step-by-step how to use Tweepy. https://towardsdatascience.com/how-to-scrape-tweets-from-twitter-59287e20f0f1

Sign in to Twitter Developer. https://developer.twitter.com/en

Full List of Tweets Object. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object

Increasing Tweepy’s standard API search limit. https://bhaskarvk.github.io/2015/01/how-to-use-twitters-search-rest-api-most-effectively./

Other Resources

1. https://github.com/indonesian-nlp/nlp-resources
2. https://github.com/irfnrdh/Awesome-Indonesia-NLP
3. https://github.com/kirralabs/indonesian-NLP-resources
4. https://huggingface.co/datasets?filter=languages%3Aid&p=0