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Resource NLP & Bahasa
https://github.com/irfnrdh/Awesome-Indonesia-NLP
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Resource NLP & Bahasa
Host: GitHub
URL: https://github.com/irfnrdh/Awesome-Indonesia-NLP
Owner: irfnrdh
Created: 2019-11-13T09:16:04.000Z (almost 5 years ago)
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Last Pushed: 2019-11-13T11:08:30.000Z (almost 5 years ago)
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Topics: awesome, indonesian-language, nlp-datasets, nlp-resources
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- Readme: README.md
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ultimate-awesome - Awesome-Indonesia-NLP - Resource NLP & Bahasa . (Other Lists / PowerShell Lists)
README

        
Awesome Indonesia NLP

====================

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

> Resouse kumpulan dataset, thesis, paper, dan artikel tentang NLP (_Natural Language Processing_) Bahasa Indonesia.

> Terinpirasi oleh para pendahulu.

### Daftar Isi

- [NLP Bahasa Indonesia](#nlp-bahasa-indonesia)

    - [Memulai](#memulai)

        - [Buku](#buku)

        - [Tutorial](#tutorial)   

    - [Dataset](#dataset)

    - [Automatic Summarization](#automatic-summarizatio)

    - [Parsing](#parsing)

    - [Part-of-speech Tagging](#part-of-speech-tagging)

    - [Stemming](#stemming)

    - [Word Sense Disambiguation](#word-sense-disambiguation)

    - [Lain-lain](#lain-lain)

    - [Software, Library, Kamus](#software-library-kamus)

    - [Dataset Berita Bahasa Indonesia](#Dataset-Berita-Indonesia)

    - [Berkontribusi](#berkontribusi)

Memulai

=======

### Materi Pengantar NLP

- [Pengantar NLP](http://lecturer.eepis-its.edu/~kangedi/materi%20kuliah/Kecerdasan%20Buatan/Bab%205%20Natural%20Language%20Processing.pdf) - Kang Edi, PENS.

- [NLTK Book](http://nltk.org/book)

- [Text Mining with R - Julia Silge and David Robinson](https://www.tidytextmining.com/)

### Artikel-Artikel Tentang NLP

- [Karena Data Gak Mungkin Bohong](https://medium.com/karena-x/karena-data-gak-mungkin-bohong-a17ff90cef87) - Jim Geovedi. 2014.

- [NLP Trend 2019](https://towardsdatascience.com/major-trends-in-nlp-a-review-of-20-years-of-acl-research-56f5520d473) - Janna, Towards Data Science.

### Jurnal

- Indonesian News Classification using Support Vector Machine (https://zenodo.org/record/1074439)

Dataset & Language modeling

=======

### Words dataset

1. [Word Sastrawi](https://github.com/sastrawi/sastrawi/tree/master/data)

1. [Word spaCy](https://github.com/explosion/spaCy/tree/master/spacy/lang/id) : id

1. [Word name](https://github.com/dominictarr/random-name) : random-name

1. [Word Indo name](https://github.com/seuriously/genderprediction/blob/master/namatraining.txt) : genderprediction

1. [Word Indo place](https://github.com/edwardsamuel/Wilayah-Administratif-Indonesia) : Wilayah-Administratif-Indonesia

1. [Word Indo place](https://github.com/pentagonal/Indonesia-Postal-Code) : Indonesia-Postal-Code

1. [Word Wiktionary](https://id.wiktionary.org/wiki) : word id

1. [Word sentiment](https://github.com/ramaprakoso/analisis-sentimen/tree/master/kamus) : analisis-sentimen

1. [Word sentiment](https://github.com/prasastoadi/ID-OpinionWords) : ID-OpinionWords

1. [Word sentiment](https://github.com/riochr17/Analisis-Sentimen-ID/tree/master/data) : Analisis-Sentimen-ID

1. [Word Acronims](https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/acronym.txt)

1. [word](https://github.com/prasastoadi/serangkai/tree/master/serangkai/kamus/data) : serangkai

### Sentences Dataset

1. [leipzig indonesian sentence collectoin](http://wortschatz.uni-leipzig.de/en/download) news articles, web articles, wikipedia data from 2008-2016

1. [wn-msa.sourceforge.net](https://sourceforge.net/p/wn-msa/tab/HEAD/tree/trunk/) Wordnet Bahasa

1. [Quran](http://tanzil.net/trans/id.indonesian) indonesian quran translation (id.muntakhab, id.jalalayn, id.indonesian)

1. [Kompas online collection](http://ilps.science.uva.nl/ilps/wp-content/uploads/sites/6/files/bahasaindonesia/kompas.zip).

   This corpus contains [Kompas online](http://www.kompas.com/) news articles from 2001-2002. See

   [here](http://ilps.science.uva.nl/resources/bahasa/) for more info and citations.

1. [Tempo online collection](http://ilps.science.uva.nl/ilps/wp-content/uploads/sites/6/files/bahasaindonesia/tempo.zip).

   This corpus contains [Tempo online](https://www.tempo.co/) news articles from 2000-2002. See

   [here](http://ilps.science.uva.nl/resources/bahasa/) for more info and citations.

1. [corpus-frog-storytelling](https://github.com/davidmoeljadi/corpus-frog-storytelling) spoken text story telling

1. [TED-Multilingual-Parallel-Corpus](https://raw.githubusercontent.com/ajinkyakulkarni14/TED-Multilingual-Parallel-Corpus/master/Monolingual_data/Indonesian.txt) Monolingual_data/Indonesian

1. [Opus](http://opus.nlpl.eu/) Opus NLPL

1. [Sealang](http://sealang.net/indonesia/) Sealang dataset

1. [Indonesian News Corpus] (https://data.mendeley.com/datasets/2zpbjs22k3/1)

1. [INDONESIAN HOAX NEWS DETECTION DATASET] (https://data.mendeley.com/datasets/p3hfgr5j3m/1)

1. [Warta Berita Online Kompas dan Tempo] (https://ilps.science.uva.nl/resources/bahasa/)

1. [Raw dataset of Indonesian news articles] (https://github.com/feryandi/Dataset-Artikel)

1. [Amazon Reviews](https://snap.stanford.edu/data/web-Amazon.html)

1. [ArXiv](http://arxiv.org/help/bulk_data_s3)

1. [BimaNLP](https://github.com/drr3d/BimaNLP/tree/old_ver/dataset)

### Tagged dataset

1. [NER](https://github.com/yohanesgultom/nlp-experiments) : yohanesgultom/nlp-experiments 1700 sentences

1. [NER](https://github.com/yusufsyaifudin/indonesia-ner) : yusufsyaifudin/indonesia-ner 1835 sentences

1. [POS-TAG](https://github.com/famrashel/idn-tagged-corpus) : famrashel/idn-tagged-corpus

1. [POS-TAG](https://github.com/pebbie/pebahasa/blob/master/resource/Corpus.crp) : pebbie/pebahasa ~600 sentence

1. [POS-TAG Parser](https://github.com/UniversalDependencies/UD_Indonesian-GSD) : UniversalDependencies/UD_Indonesian-GSD ~4477 sentence 

1. [Sentimen](https://github.com/riochr17/Analisis-Sentimen-ID/blob/master/data/training_all_random.csv) 1506 sentences 

1. [panl10n](http://www.panl10n.net/english/OutputsIndonesia2.htm) Pan Localization

## Language modeling

## POS tagging

1. [PANL10N POS tagging](http://www.panl10n.net/english/outputs/Indonesia/UI/0802/UI-1M-tagged.zip).

   This corpus has ~39K sentences and ~900K word tokens.

1. [IDN tagged corpus](https://github.com/famrashel/idn-tagged-corpus). This corpus contains

   ~10K sentences and ~250K word tokens. The POS tags are annotated manually.

## Syntactic parsing

1. [Indonesian Treebank](https://github.com/famrashel/idn-treebank). This corpus contains ~1K parsed

   sentences. (constituency parsing)

1. [UD Indonesian](https://github.com/UniversalDependencies/UD_Indonesian-GSD). This corpus is

   provided by [Universal Dependencies](http://universaldependencies.org/). Training, development,

   and testing split is already provided. (dependency parsing)

## Machine translation

1. [PANL10N EN-ID news parallel corpus](http://www.panl10n.net/english/outputs/Indonesia/BPPT/0902/BPPTIndToEngCorpusHalfM.zip).

   This corpus has sentences from news articles from several categories: economy (6K sentences),

   international (6K sentences), science (6K sentences), and sport (4K sentences).

1. [PANL10N Indonesian translation of Penn treebank](http://www.panl10n.net/english/outputs/Indonesia/UI/0802/Parallel%20Corpus.zip).

   This corpus contains Indonesian translation of the Penn treebank. In total there are ~24K

   sentences.

## Speech recognition

1. [TITML-IDN speech corpus](http://research.nii.ac.jp/src/en/TITML-IDN.html).

   The corpus contains 20 speakers (11 male and 9 female), where each of the speaker speaks 343 utterances.

   The utterances are phonetically balanced.

   

   The corpus itself is free to use for academic/non-commercial usage, but interested party should make a formal request via email to the institution.

   The procedure is listed [here](http://research.nii.ac.jp/src/en/register.html)

1. [frankydotid/Indonesian-Speech-Recognition](https://github.com/frankydotid/Indonesian-Speech-Recognition).

   A small corpus of 50 utterances by a single male speaker.

Automatic Summarization

=======================

- [Frequent Term based Text Summarization for Bahasa Indonesia](http://eprints.unsri.ac.id/3296/1/Frequent_Term_Based_Text_Summarization_For_Bahasa_Indonesia_-_E1213550.pdf)

  M.Fachrurrozi, Novi Yusliani, and Rizky Utami Yoanita. International Conference on Innovations in Engineering and Technology (ICIET'2013) Dec. 25-26, 2013 Bangkok (Thailand).

Parsing

=======

- [Analisa Struktur Kalimat Bahasa Indonesia dengan Menggunakan Pengurai Kalimat Berbasis Linguistic String Analysis](http://staf.cs.ui.ac.id/WebKuliah/IKI40921/Shelly.doc)

  Shavitri, Shelly. Undergraduate Theses for computer science, University of Indonesia, 1999.

- [INAGP : Pengurai Kalimat Bahasa Indonesia Sebagai Alat Bantu Untuk Pengembangan Aplikasi PBA](http://mail.informatika.org/~ayu/2009parser.pdf)

  Rosalina Paramita N., Dwi H. Widyantoro, Ayu Purwarianti. Undergraduate Theses from JBPTITBPP, Institute Technology Bandung, 2007.

- [Penguraian Bahasa Indonesia dengan Menggunakan Pengurai Collins](http://digilib.itb.ac.id/gdl.php?mod=browse&op=read&id=jbptitbpp-gdl-rosaariani-25714)

  Sukamto, Rosa Ariani. Tesis untuk Magister, Institut Technology Bandung, 2009.

Part-of-speech Tagging

======================

- [HMM Based Part-of-Speech Tagger for Bahasa Indonesia](http://mail.informatika.org/~ayu/2010postagger.pdf)

  Wicaksono, A. Farizki dan Purwanti, Ayu. Proceeding of 4th International Malindo (Malay and Indonesian Language) Workshop (2010).

- [Penggunaan Hidden Markov Model untuk Kompresi Kalimat](http://digilib.itb.ac.id/files/disk1/627/jbptitbpp-gdl-yudiwibiso-31314-1-2008ts-r.pdf)

  Yudi Wibisono. Graduate Thesis. Institute of Technology Bandung. 2008.

  

- [Probabilistic Part Of Speech Tagging for Bahasa Indonesia](http://www.panl10n.net/english/outputs/Indonesia/UI/0901/UI-POSTAG.pdf)

  Femphy Pisceldo, Mirna Adriani, Ruli Manurung. Third International MALINDO Workshop, colocated event ACL-IJCNLP 2009, Singapore, August 1, 2009. 

Stemming

========

- [Effective Techniques for Indonesian Text Retrieval](http://researchbank.rmit.edu.au/eserv/rmit:6312/Asian.pdf)

  Asian J. (2007). PhD thesis School of Computer Science and Information Technology RMIT University Australia.

- [Enhanced Confix Stripping Stemmer and Ants Algorithm for Classifying News Document in Indonesian Language](http://personal.its.ac.id/files/pub/2623-agusza-baru%2021%20d%20VIP%20enhanced-confix-stripping-stem.pdf)

  Arifin, A.Z., I.P.A.K. Mahendra dan H.T. Ciptaningtyas. 2009. Proceeding of International Conference on Information & Communication Technology and Systems (ICTS).

- [Implementasi Modifikasi Enhanced Confix Stripping Stemmer Untuk Bahasa Indonesia dengan Metode Corpus Based Stemming](http://digilib.its.ac.id/public/ITS-Undergraduate-14255-paperpdf.pdf)

  A. D. Tahitoe, D. Purwitasari. Institut Teknologi Sepuluh Nopember (ITS) – Surabaya.

Word Sense Disambiguation

=========================

- [Building an Indonesian WordNet](http://bahasa.cs.ui.ac.id/pub/malindo08wordnet.pdf)

  Desmond Darma Putra, Abdul Arfan and Ruli Manurung. In Proceedings of the 2nd International MALINDO Workshop. 2008.

- [English-to-Indonesian Lexical Mapping using Latent Semantic Analysis](http://bahasa.cs.ui.ac.id/pub/malindo08lsa.pdf)

  Eliza Margaretha, Franky, and Ruli Manurung. In Proceedings of the 2nd International MALINDO Workshop. 2008.

Lain-lain

=========

- [A survey of bahasa Indonesia NLP research conducted at the University of Indonesia](http://staf.cs.ui.ac.id/~maruli/pub/malindo08nlp.pdf)

  Mirna Adriani and Ruli Manurung. Faculty of Computer Science, University of Indonesia.

- [Indonesian Morphology Tool (MorphInd): Towards an Indonesian Corpus](http://ufal.mff.cuni.cz/~larasati/papers/paper6.pdf)

  Septina Dian Larasati, Vladislav Kuboˇn, and Daniel Zeman. Charles University in Prague.

- [Research Report on Local Language Computing: Development of Indonesian Language Resources and Translation System](http://www.panl10n.net/english/outputs/Indonesia/FinalReportID.pdf)

  Adriani, Mirna. Riza, Hammam. 2008.

- [Towards a Semantic Analysis of Bahasa Indonesia for Question Answering](http://bahasa.cs.ui.ac.id/pub/pacling07.pdf)

  Septina Dian Larasati and Ruli Manurung. Faculty of Computer Science. University of Indonesia. 2007.

Software, Library, Kamus

========================

- [Kateglo](http://kateglo.com/) - Kamus, Tesaurus, dan Glosarium Bahasa Indonesia.

- [Sastrawi](https://github.com/sastrawi/sastrawi) - Stemmer PHP untuk Bahasa Indonesia.

# Word reference (kemdikbud) [link](https://kbbi.kemdikbud.go.id/Beranda/Statistik)

1. Entri Dasar : 48.748 (44,64 %)

1. Kata Turunan : 26.312 (24,09 %)

1. Gabungan Kata : 30.625 (28,04 %)

1. Peribahasa : 2.040 (1,87 %)

1. Kiasan : 268 (0,25 %)

1. Ungkapan : 1.129 (1,03 %)

1. Varian : 91 (0,08 %)

1. Entri Total : 109.213 (100,00 %)

1. Makna Total : 127.775

1. Contoh Total : 29.495

1. Kategori Total : 255

1. Makna Per Entri : 1,170

1. Contoh Per Makna : 0,231

# Parallel corpus Eng-Ind

1. [parallel-corpora-en-id](https://github.com/prasastoadi/parallel-corpora-en-id/)

1. [Indonesian-English-Bilingual-Corpus](https://github.com/desmond86/Indonesian-English-Bilingual-Corpus)

1. [TALPCo](https://github.com/matbahasa/TALPCo)

1. [opus](http://opus.nlpl.eu/)

1. [Multi-Wiki](https://github.com/nguyenlab/Multi-Wiki)

# Morph

1. [MALINDO_Morph](https://github.com/matbahasa/MALINDO_Morph)

1. [morphind](http://septinalarasati.com/morphind/)

1. [INDRA](https://github.com/davidmoeljadi/INDRA)

# Crawler Data

1. [Crawler](https://github.com/harryandriyan/warta-scrap) Indonesian news portal

## Sentiment Analysis

1. [Aspect and Opinion Terms Extraction for Hotel Reviews](https://github.com/jordhy97/final_project).

    The corpus consists of 5000 hotel reviews from [Airy](https://www.airyrooms.com/) (78K tokens) with 5 labels. The paper is available on [arXiv](https://arxiv.org/abs/1908.04899).

1. [Aspect-Based Sentiment Analysis](https://github.com/annisanurulazhar/absa-playground).

    A text classification resource for multi-label aspect categorization.

## Syntactic parsing

1. [Indonesian Treebank](https://github.com/famrashel/idn-treebank). This corpus contains 1K parsed

   sentences. (constituency parsing)

1. [UD Indonesian](https://github.com/UniversalDependencies/UD_Indonesian-GSD). This corpus is

   provided by [Universal Dependencies](http://universaldependencies.org/). Training, development,

   and testing split are already provided. (dependency parsing)

## Machine translation

1. [PANL10N EN-ID news parallel corpus](http://www.panl10n.net/english/outputs/Indonesia/BPPT/0902/BPPTIndToEngCorpusHalfM.zip).

   This corpus has sentences from news articles from several categories: economy (6K sentences),

   international (6K sentences), science (6K sentences), and sport (4K sentences).

1. [PANL10N Indonesian translation of Penn treebank](http://www.panl10n.net/english/outputs/Indonesia/UI/0802/Parallel%20Corpus.zip).

   This corpus contains Indonesian translation of the Penn treebank. In total there are 24K

   sentences.

## Word Normalization

1. [Colloquial Indonesian Lexicon](https://github.com/nasalsabila/kamus-alay).

    This lexicon consists of 3592 unique colloquial tokens that are mapped onto 1742 unique lemmas. The full description of this lexicon can be seen in the [paper](https://ieeexplore.ieee.org/abstract/document/8629151).

## Text Summarization

1. [IndoSum](https://github.com/kata-ai/indosum).

    A collection of 20K online news article-summary pairs belonging to 6 categories and 10 sources.

    It has both abstractive summaries and extractive labels.

## Text Classification

1. [SMS Spam](http://nlp.yuliadi.pro/static/dataset_sms_spam_bhs_indonesia.zip).

   This corpus contains 1143 sentences that have been labeled with normal message, fraud, promotion. It is provided by http://nlp.yuliadi.pro/dataset

1. [Hate Speech Detection](https://github.com/ialfina/id-hatespeech-detection).

    This dataset consists of 713 tweets in the Indonesian language with 453 non hate speech and 260 hate speech tweets.

1. [Abusive Language Detection](https://github.com/okkyibrohim/id-abusive-language-detection).

    A collection of tweets for abusive language detection in Indonesian social media. It consists of two types of labelling, abusive/not abusive and not abusive/abusive but not offensive/offensive. It also has its own colloquial Indonesian lexicon.

   

## Speech recognition

1. [TITML-IDN speech corpus](http://research.nii.ac.jp/src/en/TITML-IDN.html).

   The corpus contains 20 speakers (11 male and 9 female), where each of the speaker speaks 343 utterances.

   The utterances are phonetically balanced.

   The corpus itself is free to use for academic/non-commercial usage, but interested party should make a formal request via email to the institution.

   The procedure is listed [here](http://research.nii.ac.jp/src/en/register.html).

1. [Indonesian Speech Recognition](https://github.com/frankydotid/Indonesian-Speech-Recognition).

   A small corpus of 50 utterances by a single male speaker. Disclaimer: This is a school project, do not use it for any important tasks. The author is not responsible for the undesired results of using the data provided here.

1. [CMU Wilderness Multilingual Speech Dataset](https://github.com/festvox/datasets-CMU_Wilderness).

   A dataset of over 700 different languages providing audio, aligned texts, and word pronunciations.

   One of the languages is Indonesian. The utterances are read from the bible, which is recorded by [bible.is](bible.is).

   

### Free Books

### Courses

1.	[Natural Language Processing - Coursera](https://www.coursera.org/learn/language-processing)

2.	[Nautral Language Processing - Edx](https://www.edx.org/course/natural-language-processing-nlp)

3.	[Oxford CS Deep NLP](https://github.com/oxford-cs-deepnlp-2017)

### Videos and Lectures

1.	[2016 CS224D Deep Learning For Natural Language Processing Lecture Videos](https://www.youtube.com/playlist?list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam)

2.	[Natural Language Processing](https://www.youtube.com/watch?v=mieV29RVpuQ&list=PL0ap34RKaADMjqjdSkWolD-W2VSCyRUQC)

### Papers

1.	[Breaking Sticks and Ambiguities with Adaptive Skip-gram](http://arxiv.org/abs/1502.07257)

2.	[Distributed Representations of Words and Phrases and their Compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)

3.	[Learning the Dimensionality of Word Embeddings](http://arxiv.org/abs/1511.05392)

4.	[Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://papers.nips.cc/paper/6810-emergence-of-language-with-multi-agent-games-learning-to-communicate-with-sequences-of-symbols.pdf)

5.	[Skip Thought Vectors](http://arxiv.org/abs/1506.06726)

### Tutorials

1.	[Natural Language Processing](http://aiplaybook.a16z.com/docs/guides/nlp)

2.	[Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK](https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a)

3.	[Multi-Class Classification Tutorial with the Keras Deep Learning Library](https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/)

4.	[Topic Modeling with Scikit Learn](https://medium.com/mlreview/topic-modeling-with-scikit-learn-e80d33668730)

5.	[Data Science with Python & R: Sentiment Classification Using Linear Methods](https://www.codementor.io/jadianes/data-science-python-r-sentiment-classification-machine-learning-du107otfg)

### Sample Code

1.	[Sentiment](https://github.com/vivekn/sentiment)

2.	[Prediksi Gender Nama](https://github.com/vickydasta/prediksi-gender-nama)

3.	[Topic Modeling](https://github.com/piskvorky/topic_modeling_tutorial)

4.	[POS Tagging NLTK (Bahasa Indonesia)](https://github.com/mrrizal/POS_Tag_Indonesian)

5.	[Naive Bayes Document Classifier (Bahasa Indonesia)](https://github.com/mrrizal/Document_Classifier)

### Datasets

### Libraries

1.	[NLTK](http://www.nltk.org/)

2.	[Gensim](https://github.com/RaRe-Technologies/gensim)

3.	[TextBlob](https://github.com/sloria/textblob)

4.	[Spacy](https://github.com/explosion/spaCy)

5.	[Sastrawi](https://github.com/sastrawi/sastrawi)

6.	[Nalapa](https://github.com/anpandu/nalapa)

7.  [Polyglot](https://github.com/aboSamoor/polyglot)

## Contributing

Jika ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia.

## Frequently Ask Question (FAQ)

FAQ menjawab pertanyaan pertanyaan umum terkait repository ini mulai dari _naming convention_, pertanyaan dasar hingga pertanyaan lanjut.

# Awesome NLP Papers

This is a collection/reading-list of awesome Natural Language Processing papers sorted by date.

### 2018

- [X] **Unsupervised Machine Translation Using Monolingual Corpora Only**, Lample et al.

[`Paper`](https://arxiv.org/abs/1711.00043)

- [X] **On the Dimensionality of Word Embeddings**, Yin et al.

[`Paper`](https://papers.nips.cc/paper/7368-on-the-dimensionality-of-word-embedding)

- [X] **An efficient framework for learning sentence representations**, Logeswaran et al.

[`Paper`](https://arxiv.org/abs/1803.02893)

- [X] **Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation**, Akira Utsumi

[`Paper`](http://aclweb.org/anthology/D18-1520)

- [X] **Domain Adapted Word Embeddings for Improved Sentiment Classification**, Sarma et al.

[`Paper`](https://arxiv.org/abs/1805.04576)

- [X] **In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition**, Sheikhshab et al.

[`Paper`](http://www.aclweb.org/anthology/W18-5618)

- [X] **Generalizing Word Embeddings using Bag of Subwords**, Zhao et al.

[`Paper`](https://arxiv.org/abs/1809.04259)

- [X] **What's in Your Embedding, And How It Predicts Task Performance**, Rogers et al.

[`Paper`](http://www.aclweb.org/anthology/C18-1228)

- [X] **On Learning Better Word Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data** Wang et al.

[`Paper`](http://www.aclweb.org/anthology/W18-2323)

- [X] **Predicting and interpreting embeddings for out of vocabulary words in downstream tasks**, Garneau et al.

[`Paper`](http://www.aclweb.org/anthology/W18-5439)

- [X] **Addressing Low-Resource Scenarios with Character-aware Embeddings**, Papay et al.

[`Paper`](http://www.aclweb.org/anthology/W18-1204)

- [X] **Domain Adaptation for Disease Phrase Matching with Adversarial Networks**, Liu et al.

[`Paper`](http://www.aclweb.org/anthology/W18-2315)

- [X] **Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus**, Komiya et al.

[`Paper`](http://www.aclweb.org/anthology/W18-3408)

- [X] **BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**, Devlin et al.

[`Paper`](https://arxiv.org/abs/1810.04805)

- [X] **Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes**, Zhang et al.

[`Paper`](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961810/)

- [ ] **Evaluation of sentence embeddings in downstream and linguistic probing tasks**, Perone et al.

[`Paper`](https://arxiv.org/abs/1806.06259)

- [ ] **Universal Sentence Encoder**, Cer et al.

[`Paper`](https://arxiv.org/abs/1803.11175)

- [X] **Deep Contextualized Word Representations**, Peters et al.

[`Paper`](https://arxiv.org/abs/1802.05365)

- [X] **Learned in Translation: Contextualized Word Vectors**, McCann et al.

[`Paper`](https://arxiv.org/abs/1708.00107)

- [X] **Concatenated p-mean Word Embeddings as Universal Cross-Lingual Sentence Representations**, Rücklé et al.

[`paper`](https://arxiv.org/abs/1803.01400)

- [X] **A Compressed Sensing View of Unsupervised Text Embeddings, Bag-Of-n-Grams, and LSTMs**, Arora et al.

[`Paper`](https://openreview.net/pdf?id=B1e5ef-C-)

### 2017:

- [X] **Attention Is All You Need**, Vaswani et al.

[`Paper`](http://papers.nips.cc/paper/7181-attention-is-all-you-need)

- [X] **Skip-Gram – Zipf + Uniform = Vector Additivity**, Gittens et al.

[`Paper`](http://www.aclweb.org/anthology/P17-1007)

- [X] **A Simple but Tough-to-beat Baseline for Sentence Embeddings**, Arora et al.

[`Paper`](https://openreview.net/pdf?id=SyK00v5xx)

- [X] **Fast and Accurate Entity Recognition with Iterated Dilated Convolutions**, Strubell et al.

[`Paper`](https://arxiv.org/abs/1702.02098)

- [X] **Advances in Pre-Training Distributed Word Representations**, Mikolov et al.

[`Paper`](https://arxiv.org/abs/1712.09405)

- [X] **Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets**, Dror et al.

[`Paper`](https://arxiv.org/abs/1709.09500)

### 2016:

- [X] **Towards Universal Paraphrastic Sentence Embeddings**, Wieting et al.

[`Paper`](https://arxiv.org/abs/1511.08198)

- [X] **Bag of Tricks for Efficient Text Classification**, Joulin et al.

[`Paper`](https://arxiv.org/abs/1607.01759)

- [X] **Enriching Word Vectors with Subword Information**, Bojanowski et al.

[`Paper`](https://arxiv.org/abs/1607.04606)

- [X] **Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP**, Kirk Roberts

[`Paper`](http://www.aclweb.org/anthology/W16-4208)

- [X] **How to Train Good Word Embeddings for Biomedical NLP**, Chiu et al.

[`Paper`](http://www.aclweb.org/anthology/W16-2922)

- [X] **Log-Linear Models, MEMMs, and CRFs**, Michael Collins

[`Paper`](http://www.cs.columbia.edu/~mcollins/crf.pdf)

- [X] **Counter-fitting Word Vectors to Linguistic Constraints**, Mrkšić et al.

[`Paper`](https://arxiv.org/abs/1603.00892)

- [X] **Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation**, Wu et al.

[`Paper`](https://arxiv.org/abs/1609.08144)

### 2015:

- [ ] **Semi-supervised Sequence Learning**, Dai et al.

[`Paper`](https://arxiv.org/abs/1511.01432)

- [X] **Evaluating distributed word representations for capturing semantics of biomedical concepts**, Th et al.

[`Paper`](http://www.aclweb.org/anthology/W15-3820)

### 2014:

- [X] **GloVe: Global Vectors for Word Representation**, Pennington et al.

[`Paper`](https://www.aclweb.org/anthology/D14-1162)

- [X] **Linguistic Regularities in Sparse and Explicit Word Representations**, Levy and Goldberg.

[`Paper`](https://www.cs.bgu.ac.il/~yoavg/publications/conll2014analogies.pdf)

- [X] **Neural Word Embedding as Implicit Matrix Factorization**, Levy and Goldberg.

[`Paper`](https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf)

- [X] **word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method**, Goldberg and Levy.

[`Paper`](https://arxiv.org/abs/1402.3722)

- [X] **What’s in a p-value in NLP?**, Søgaard et al.

[`Paper`](http://www.aclweb.org/anthology/W14-1601)

- [X] **How transferable are features in deep neural networks?**, Yosinski et al.

[`Paper`](http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-n%E2%80%A6)

- [X] **Improving lexical embeddings with semantic knowledge**, Yu et al.

[`Paper`](http://www.aclweb.org/anthology/P14-2089)

- [X] **Retrofitting word vectors to semantic lexicons**, Faruqui et al.

[`Paper`](https://arxiv.org/abs/1411.4166)

### 2013:

- [X] **Efficient Estimation of Word Representations in Vector Space**, Mikolov et al.

[`Paper`](https://arxiv.org/pdf/1301.3781.pdf)

- [X] **Linguistic Regularities in Continuous Space Word Representations**, Mikolov et al.

[`Paper`](https://www.aclweb.org/anthology/N13-1090)

- [X] **Distributed Representations of Words and Phrases and their Compositionality**, Mikolov et al.

[`Paper`](https://arxiv.org/abs/1310.4546)

### 2012:

- [X] **An Empirical Investigation of Statistical Significance in NLP**, Berg-Kirkpatrick et al.

[`Paper`](https://dl.acm.org/citation.cfm?id=2391058)

### 2010:

- [X] **Word representations: A simple and general method for semi-supervised learning**, Turian et al.

[`Paper`](https://dl.acm.org/citation.cfm?id=1858721)

### 2008:

- [ ] **A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning**, Collobert and Weston.

[`Paper`](https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf)

### 2006:

- [X] **Domain adaptation with structural correspondence learning**, Blitzer et al.

[`Paper`](https://dl.acm.org/citation.cfm?id=1610094)

### 2003:

- [X] **A Neural Probabilistic Language Model**, Bengio et al.

[`Paper`](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)

### 1986:

- [ ] **Distributed Representations**, Hinton et al.

[`Paper`](https://web.stanford.edu/~jlmcc/papers/PDP/Chapter3.pdf)