{"id":13615217,"url":"https://github.com/louisowen6/NLP_bahasa_resources","last_synced_at":"2025-04-13T21:30:32.465Z","repository":{"id":40393146,"uuid":"251623447","full_name":"louisowen6/NLP_bahasa_resources","owner":"louisowen6","description":"A Curated List of Dataset and Usable Library Resources for NLP in Bahasa 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Language Processing","Others"],"sub_categories":[],"readme":"# NLP Bahasa Indonesia Resources\n\nThis repository provides link to useful dataset and another resources for NLP in Bahasa Indonesia. \n\n*Last Update: 15 Mar 2022*\n\n## Table of contents\n  * [Corpus](#corpus)\n    * [Named Entity Recognition](#named-entity-recognition)\n    * [POS-Tagging](#pos-tagging)\n    * [Question and Answering](#question-and-answering)\n    * [Paraphrasing](#paraphrasing)\n    * [Text Summarization](#text-summarization)\n    * [Hate-speech](#hate-speech)\n    * [Word Analogy](#word-analogy)\n    * [Formal-Informal](#formal-informal)\n    * [Multilingual Parallel](#multilingual-parallel)\n    * [Unsupervised Corpus](#unsupervised-corpus)\n    * [Voice-Text](#voice-text)\n    * [Puisi and Pantun](#puisi-and-pantun)\n * [Dictionary](#dictionary) \n    * [Synonym](#synonym)\n    * [Sentiment](#sentiment)\n    * [Position or Degree](#position-or-degree)\n    * [Root Words](#root-words)\n    * [Slang Words](#slang-words)\n    * [Stop Words](#stop-words)\n    * [Swear Words](#swear-words)\n    * [Composite Words](#composite-words)\n    * [Number Words](#number-words)\n    * [Calendar Words](#calendar-words)\n    * [Emoticon](#emoticon)\n    * [Acronym](#acronym)\n    * [Indonesia Region](#indonesia-region)\n    * [Country](#country)\n    * [Region](#region)\n    * [Title of Name](#title-of-name)\n    * [Gender by Name](#gender-by-name)\n    * [Organization](#organization)\n*  [Articles and Papers](#articles-and-papers)\n    * [POS-Tagging](#pos-tagging)\n    * [Word Embedding](#word-embedding)\n    * [Topic Analysis](#topic-analysis)\n    * [Text Classification](#text-classification)\n*  [Pre-trained Models](#pre-trained-models)\n*  [Usable Library](#usable-library)\n*  [Spelling Correction](#spelling-correction)\n*  [Twitter Scraping](#twitter-scrapping)\n*  [Other Resources](#other-resourceS)\n\n\n## [Corpus](corpus)\n\n### [Named Entity Recognition](corpus/named-entity-recognition)\n1) Product NER. https://github.com/dziem/proner-labeled-text\n2) NER-grit. https://github.com/grit-id/nergrit-corpus\n\n### [POS-Tagging](corpus/pos-tagging)\n1) IDN Tagged Corpus. https://github.com/famrashel/idn-tagged-corpus\n2) Indonesian Part-of-Speech (POS) Tagging. https://github.com/kmkurn/id-pos-tagging/blob/master/data/dataset.tar.gz\n\n### [Question and Answering](corpus/question-and-answering)\n1) TydiQA. https://github.com/google-research-datasets/tydiqa\n\n### [Paraphrasing](corpus/paraphrasing)\n1) Quora Paraphrasing. https://github.com/louisowen6/quora_paraphrasing_id\n2) Paraphrase Adversaries from Word Scrambling. https://github.com/Wikidepia/indonesian_datasets/tree/master/paraphrase/paws\n\n### [Text Summarization](corpus/text-summarization)\n1) Indosum. https://github.com/kata-ai/indosum\n2) Liputan6. https://huggingface.co/datasets/id_liputan6\n\n### [Hate-speech](corpus/hate-speech)\n1) ID Multi Label Hate Speech. https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection\n\n### [Word Analogy](corpus/word-analogy)\n1) KAWAT. https://github.com/kata-ai/kawat\n\n### [Formal-Informal](corpus/formal-informal)\n1) STIF-Indonesia. https://github.com/haryoa/stif-indonesia\n2) IndoCollex. https://github.com/haryoa/indo-collex\n3) https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection/blob/master/new_kamusalay.csv\n\n### [Multilingual Parallel](corpus/multilingual-parallel)\n1) https://huggingface.co/datasets/alt\n2) https://opus.nlpl.eu/bible-uedin.php\n3) http://www.statmt.org/cc-aligned/\n4) https://huggingface.co/datasets/id_panl_bppt\n5) https://huggingface.co/datasets/open_subtitles\n6) https://huggingface.co/datasets/opus100\n7) https://huggingface.co/datasets/tapaco\n8) https://huggingface.co/datasets/wiki_lingua\n\n### [Unsupervised Corpus](corpus/unsupervised-corpus)\n1) OSCAR. https://oscar-corpus.com/\n2) Online Newspaper. https://github.com/feryandi/Dataset-Artikel\n3) IndoNLU. https://huggingface.co/datasets/indonlu\n4) IndoNLG. https://github.com/indobenchmark/indonlg\n5) IndoNLI. https://github.com/ir-nlp-csui/indonli\n6) IndoBERTweet. https://github.com/indolem/IndoBERTweet\n7) http://data.statmt.org/cc-100/\n8) https://huggingface.co/datasets/id_clickbait\n9) https://huggingface.co/datasets/id_newspapers_2018\n10) https://opus.nlpl.eu/QED.php\n\n### [Voice-Text](corpus/voice-text)\n1) https://huggingface.co/datasets/common_voice\n2) https://huggingface.co/datasets/covost2\n\n### [Puisi and Pantun](corpus/puisi-and-pantun)\n1) https://github.com/ilhamfp/puisi-pantun-generator\n\n\n## [Dictionary](dictionary)\n\n### [Synonym](dictionary/synonym)\n1) https://github.com/victoriasovereigne/tesaurus\n\n### [Sentiment](dictionary/sentiment)\n1) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negatif_ta2.txt\n2) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negative_add.txt\n3) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negative_keyword.txt\n4) (Negative) https://github.com/masdevid/ID-OpinionWords/blob/master/negative.txt\n5) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positif_ta2.txt\n6) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positive_add.txt\n7) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positive_keyword.txt\n8) (Positive) https://github.com/masdevid/ID-OpinionWords/blob/master/positive.txt\n9) (Score) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/sentimentword.txt\n10) (InSet Lexicon) https://github.com/fajri91/InSet [[Paper](https://www.researchgate.net/publication/321757985_InSet_Lexicon_Evaluation_of_a_Word_List_for_Indonesian_Sentiment_Analysis_in_Microblogs)]\n11) (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](https://www.researchgate.net/publication/338409000_Dataset_Indonesia_untuk_Analisis_Sentimen)]\n12) https://huggingface.co/datasets/senti_lex\n\n### [Position or Degree](dictionary/position-or-degree)\n1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/psuf.txt\n2) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lldr.txt\n3) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/opos.txt\n4) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ptit.txt\n\n### [Root Words](dictionary/root-words)\n1) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/rootword.txt\n2) https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.original.txt\n3) https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.txt\n4) https://github.com/prasastoadi/serangkai/blob/master/serangkai/kamus/data/kamus-kata-dasar.csv\n\nI have made the [combined root words list](https://github.com/louisowen6/NLP_bahasa_resources/blob/master/combined_root_words.txt) from all of the above repositories.\n \n### [Slang Words](dictionary/slang-words)\n1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/kbba.txt\n2) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/slangword.txt\n3) https://github.com/panggi/pujangga/blob/master/resource/formalization/formalizationDict.txt\n\nI have made the [combined slang words dictionary](https://github.com/louisowen6/NLP_bahasa_resources/blob/master/combined_slang_words.txt) from all of the above repositories.\n\n### [Stop Words](dictionary/stop-words)\n1) https://github.com/yasirutomo/python-sentianalysis-id/blob/master/data/feature_list/stopwordsID.txt\n2) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/stopword.txt\n3) https://github.com/abhimantramb/elang/tree/master/word2vec/utils/stopwords-list\n\nI have made the [combined stop words list](https://github.com/louisowen6/NLP_bahasa_resources/blob/master/combined_stop_words.txt) from all of the above repositories.\n\n### [Swear Words](dictionary/swear-words)\n1) https://github.com/abhimantramb/elang/blob/master/word2vec/utils/swear-words.txt\n\n### [Composite Words](dictionary/composite-words)\n1) https://github.com/panggi/pujangga/blob/master/resource/tokenizer/compositewords.txt\n\n### [Number Words](dictionary/number-words)\n1) https://github.com/panggi/pujangga/blob/master/resource/netagger/morphologicalfeature/number.txt\n\n### [Calendar Words](dictionary/calendar-words)\n1) https://github.com/onlyphantom/elang/blob/master/build/lib/elang/word2vec/utils/negative/calendar-words.txt\n\n### [Emoticon](dictionary/emoticon)\n1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/emoticon.txt\n2) https://github.com/jolicode/emoji-search/blob/master/synonyms/cldr-emoji-annotation-synonyms-id.txt\n3) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/emoticon.txt\n\n### [Acronym](dictionary/acronym)\n1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/acronym.txt\n2) https://github.com/panggi/pujangga/blob/master/resource/sentencedetector/acronym.txt\n3) https://id.wiktionary.org/wiki/Lampiran:Daftar_singkatan_dan_akronim_dalam_bahasa_Indonesia#A\n\n### [Indonesia Region](dictionary/indonesia-region)\n1) https://github.com/abhimantramb/elang/blob/master/word2vec/utils/indonesian-region.txt\n2) https://github.com/edwardsamuel/Wilayah-Administratif-Indonesia/tree/master/csv\n3) https://github.com/pentagonal/Indonesia-Postal-Code/tree/master/Csv\n\n### [Country](dictionary/country)\n1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/country.txt\n\n### [Region](dictionary/region)\n1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lpre.txt\n\n### [Title of Name](dictionary/title-of-name)\n1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ppre.txt\n\n### [Gender by Name](dictionary/gender-by-name)\n1) https://github.com/seuriously/genderprediction/blob/master/namatraining.txt\n\n### [Organization](dictionary/organization)\n1) https://github.com/panggi/pujangga/blob/master/resource/reference/opre.txt\n\n## [Articles and Papers](articles-and-papers)\n\n### [POS-Tagging](articles-and-papers/pos-tagging)\n1) https://medium.com/@puspitakaban/pos-tagging-bahasa-indonesia-dengan-flair-nlp-c12e45542860\n2) Manually Tagged Indonesian Corpus [[Paper](http://bahasa.cs.ui.ac.id/postag/downloads/Designing%20an%20Indonesian%20Part%20of%20speech%20Tagset.pdf)] [[GitHub](https://github.com/famrashel/idn-tagged-corpus)]\n\n### [Word Embedding](articles-and-papers/word-embedding)\n1) (FastText). https://structilmy.com/2019/08/membuat-model-word-embedding-fasttext-bahasa-indonesia/\n2) (Word2Vec). https://yudiwbs.wordpress.com/2018/03/31/word2vec-wikipedia-bahasa-indonesia-dengan-python-gensim/\n\n### [Topic Analysis](articles-and-papers/topic-analysis)\n1) (Introduction to LSA \u0026 LDA). https://monkeylearn.com/blog/introduction-to-topic-modeling/\n2) (Introduction to LDA w/ Code \u0026 Tips). https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/\n3) (Topic Modeling Methods Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper_21-A_Survey_of_Topic_Modeling_in_Text_Mining.pdf\n4) (Original LDA Paper). http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf\n5) (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\n6) (Original CTM Paper). http://people.ee.duke.edu/~lcarin/Blei2005CTM.pdf\n7) (CTM Python Library). https://pypi.org/project/tomotopy/; https://github.com/kzhai/PyCTM\n8) (Gaussian LDA Paper). https://www.aclweb.org/anthology/P15-1077.pdf\n9) (Gaussian LDA Library). https://github.com/rajarshd/Gaussian_LDA\n10) (Temporal Topic Modeling Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper_21-A_Survey_of_Topic_Modeling_in_Text_Mining.pdf\n11) (TOT: A Non-Markov Continuous-Time Model of Topical Trends Paper). https://people.cs.umass.edu/~mccallum/papers/tot-kdd06s.pdf\n12) (TOT Library). https://github.com/ahmaurya/topics_over_time  \n13) (Example of LDA in Bahasa Project Code). https://github.com/kirralabs/text-clustering\n\n### [Text Classification](articles-and-papers/text-classification)\n#### Zero-shot Learning\n1) (Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach) https://arxiv.org/pdf/1909.00161.pdf | https://github.com/yinwenpeng/BenchmarkingZeroShot\n2) (Integrating Semantic Knowledge to Tackle Zero-shot Text Classification) https://arxiv.org/abs/1903.12626 | https://github.com/JingqingZ/KG4ZeroShotText\n3) (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/\n4) (Zero-shot Text Classification With Generative Language Models) https://arxiv.org/abs/1912.10165 | https://amitness.com/2020/06/zero-shot-classification-via-generation/\n5) (Zero-shot User Intent Detection via Capsule Neural Networks) https://arxiv.org/abs/1809.00385 | https://github.com/congyingxia/ZeroShotCapsule\n\n#### Few-shot Learning\n1) (Few-shot Text Classification with Distributional Signatures) https://arxiv.org/pdf/1908.06039.pdf | https://github.com/YujiaBao/Distributional-Signatures\n2) (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\n3) (Induction Networks for Few-Shot Text Classification) https://arxiv.org/pdf/1902.10482v2.pdf | https://github.com/zhongyuchen/few-shot-learning\n\n## [Pre-trained Models](pre-trained-models)\n1) Indo-BERT. https://github.com/indobenchmark/indonlu \u0026 https://huggingface.co/indobenchmark/indobert-base-p1\n2) Indo-BERTweet. https://github.com/indolem/IndoBERTweet \u0026 https://huggingface.co/indolem/indobertweet-base-uncased\n3) Transformer-based Pre-trained Model in Bahasa. https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers\n4) 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'\n5) https://github.com/meisaputri21/Indonesian-Twitter-Emotion-Dataset. [[Paper](https://www.researchgate.net/publication/330674171_Emotion_Classification_on_Indonesian_Twitter_Dataset/link/5c4ea13a458515a4c745850d/download)]\n6) https://github.com/Kyubyong/wordvectors\n7) https://drive.google.com/uc?id=0B5YTktu2dOKKNUY1OWJORlZTcUU\u0026export=download\n8) https://github.com/deryrahman/word2vec-bahasa-indonesia\n9) https://sites.google.com/site/rmyeid/projects/polyglot\n\n## [Usable Library](usable-library)\n1) Pujangga: Indonesian Natural Language Processing REST API. https://github.com/panggi/pujangga \n2) Sastrawi Stemmer Bahasa Indonesia. https://github.com/sastrawi/sastrawi\n3) NLP-ID. https://github.com/kumparan/nlp-id\n4) MorphInd: Indonesian Morphological Analyzer. http://septinalarasati.com/morphind/\n5) INDRA: Indonesian Resource Grammar. https://github.com/davidmoeljadi/INDRA\n6) Typo Checker. https://github.com/mamat-rahmat/checker_id\n7) Multilingual NLP Package. https://github.com/flairNLP/flair\n9) spaCy [[GitHub](https://github.com/explosion/spaCy)] [[Tutorial](https://bagas.me/spacy-bahasa-indonesia.html)]\n9) https://github.com/yohanesgultom/nlp-experiments\n10) https://github.com/yasirutomo/python-sentianalysis-id\n11) https://github.com/riochr17/Analisis-Sentimen-ID\n12) https://github.com/yusufsyaifudin/indonesia-ner\n\n## [Spelling Correction](spelling-correction)\nYou can adjust [this code](https://norvig.com/spell-correct.html?utm_medium=social\u0026utm_source=linkedin\u0026utm_campaign=postfity\u0026utm_content=postfity50031) with Bahasa corpus to do the spelling correction\n\n## [Twitter Scraping](twitter-scrapping)\n1) GetOldTweets3. https://github.com/Mottl/GetOldTweets3\n\nUsage:\n```bash\nimport GetOldTweets3 as got\ntweetCriteria=got.manager.TweetCriteria().setQuerySearch('#CoronaVirusIndonesia').setSince(\"2020-01-01\").setUntil(\"2020-03-05\").setNear(\"Jakarta, Indonesia\").setLang(\"id\")\ntweets=got.manager.TweetManager.getTweets(tweetCriteria)\nfor tweet in tweets:\n\tprint(tweet.username)\n\tprint(tweet.text)\n\tprint(tweet.date)\n\tprint(\"tweet.to\")\n\tprint(\"tweet.retweets\")\n\tprint(\"tweet.favorites\")\n\tprint(\"tweet.mentions\")\n\tprint(\"tweet.hashtags\")\n\tprint(\"tweet.geo\")\n ```\n\n2) Tweepy. http://docs.tweepy.org/en/latest/\n\nStep-by-step how to use Tweepy. https://towardsdatascience.com/how-to-scrape-tweets-from-twitter-59287e20f0f1\n\nSign in to Twitter Developer. https://developer.twitter.com/en\n\nFull List of Tweets Object. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object\n\nIncreasing Tweepy’s standard API search limit. https://bhaskarvk.github.io/2015/01/how-to-use-twitters-search-rest-api-most-effectively./\n\n## [Other Resources](other-resourceS)\n1) https://github.com/indonesian-nlp/nlp-resources\n2) https://github.com/irfnrdh/Awesome-Indonesia-NLP\n3) https://github.com/kirralabs/indonesian-NLP-resources\n4) https://huggingface.co/datasets?filter=languages%3Aid\u0026p=0\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flouisowen6%2FNLP_bahasa_resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flouisowen6%2FNLP_bahasa_resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flouisowen6%2FNLP_bahasa_resources/lists"}