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https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#natural-language-processing-tasks-and-selected-references
YJ Choehttps://github.com/yjchoe
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#anaphora-resolution
Coreference Resolutionhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#coreference-resolution
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#automated-essay-scoring
Automatic Text Scoring Using Neural Networkshttps://arxiv.org/abs/1606.04289
A Neural Approach to Automated Essay Scoringhttp://www.aclweb.org/old_anthology/D/D16/D16-1193.pdf
Kaggle: The Hewlett Foundation: Automated Essay Scoringhttps://www.kaggle.com/c/asap-aes
EASE (Enhanced AI Scoring Engine)https://github.com/edx/ease
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#automatic-speech-recognition
Speech recognitionhttps://en.wikipedia.org/wiki/Speech_recognition
Deep Speech 2: End-to-End Speech Recognition in English and Mandarinhttps://arxiv.org/abs/1512.02595
WaveNet: A Generative Model for Raw Audiohttps://arxiv.org/abs/1609.03499
A TensorFlow implementation of Baidu's DeepSpeech architecturehttps://github.com/mozilla/DeepSpeech
Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition using DeepMind's WaveNethttps://github.com/buriburisuri/speech-to-text-wavenet
The 5th CHiME Speech Separation and Recognition Challengehttp://spandh.dcs.shef.ac.uk/chime_challenge/
The 5th CHiME Speech Separation and Recognition Challengehttp://spandh.dcs.shef.ac.uk/chime_challenge/download.html
CSTR VCTK Corpushttp://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html
LibriSpeech ASR corpushttp://www.openslr.org/12/
Switchboard-1 Telephone Speech Corpushttps://catalog.ldc.upenn.edu/ldc97s62
TED-LIUM Corpushttp://www-lium.univ-lemans.fr/en/content/ted-lium-corpus
Open Speech and Language Resourceshttp://www.openslr.org/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#automatic-summarisation
Automatic summarizationhttps://en.wikipedia.org/wiki/Automatic_summarization
Automatic Text Summarizationhttps://www.amazon.com/Automatic-Text-Summarization-Juan-Manuel-Torres-Moreno/dp/1848216688/ref=sr_1_1?s=books&ie=UTF8&qid=1507782304&sr=1-1&keywords=Automatic+Text+Summarization
Text Summarization Using Neural Networkshttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.823.8025&rep=rep1&type=pdf
Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarizationhttps://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9414/9520
Text Analytics Conferences (TAC)https://tac.nist.gov/data/index.html
Document Understanding Conferences (DUC)http://www-nlpir.nist.gov/projects/duc/data.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#coreference-resolution
Coreference Resolutionhttps://nlp.stanford.edu/projects/coref.shtml
Deep Reinforcement Learning for Mention-Ranking Coreference Modelshttps://arxiv.org/abs/1609.08667
Improving Coreference Resolution by Learning Entity-Level Distributed Representationshttps://arxiv.org/abs/1606.01323
CoNLL 2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNoteshttp://conll.cemantix.org/2012/task-description.html
CoNLL 2011 Shared Task: Modeling Unrestricted Coreference in OntoNoteshttp://conll.cemantix.org/2011/task-description.html
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues (In Progress)https://competitions.codalab.org/competitions/17310
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#entity-linking
Named Entity Disambiguationhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#named-entity-disambiguation
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#grammatical-error-correction
Neural Network Translation Models for Grammatical Error Correctionhttps://arxiv.org/abs/1606.00189
Adapting Sequence Models for Sentence Correctionhttp://aclweb.org/anthology/D17-1297
CoNLL-2013 Shared Task: Grammatical Error Correctionhttp://www.comp.nus.edu.sg/~nlp/conll13st.html
CoNLL-2014 Shared Task: Grammatical Error Correctionhttp://www.comp.nus.edu.sg/~nlp/conll14st.html
NUS Non-commercial research/trial corpus licensehttp://www.comp.nus.edu.sg/~nlp/conll14st/nucle_license.pdf
Lang-8 Learner Corporahttp://cl.naist.jp/nldata/lang-8/
Cornell Movie--Dialogs Corpushttp://www.cs.cornell.edu/%7Ecristian/Cornell_Movie-Dialogs_Corpus.html
Deep Text Correctorhttps://github.com/atpaino/deep-text-corrector
deep grammarhttp://deepgrammar.com/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#grapheme-to-phoneme-conversion
Grapheme-to-Phoneme Models for (Almost) Any Languagehttps://pdfs.semanticscholar.org/b9c8/fef9b6f16b92c6859f6106524fdb053e9577.pdf
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learninghttps://arxiv.org/pdf/1605.03832.pdf
Multitask Sequence-to-Sequence Models for Grapheme-to-Phoneme Conversionhttps://pdfs.semanticscholar.org/26d0/09959fa2b2e18cddb5783493738a1c1ede2f.pdf
Sequence-to-Sequence G2P toolkithttps://github.com/cmusphinx/g2p-seq2seq
Multilingual Pronunciation Datahttps://drive.google.com/drive/folders/0B7R_gATfZJ2aWkpSWHpXUklWUmM
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#humor-and-sarcasm-detection
Automatic Sarcasm Detection: A Surveyhttps://arxiv.org/abs/1602.03426
Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personalhttp://aclweb.org/anthology/D17-1051
Sarcasm Detection on Twitter: A Behavioral Modeling Approachhttp://ai2-s2-pdfs.s3.amazonaws.com/67b5/9db00c29152d8e738f693f153e1ab9b43466.pdf
SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humorhttp://alt.qcri.org/semeval2017/task6/
SemEval-2017 Task 7: Detection and Interpretation of English Punshttp://alt.qcri.org/semeval2017/task7/
Sarcastic comments from Reddithttps://www.kaggle.com/danofer/sarcasm/
Sarcasm Corpus V2https://nlds.soe.ucsc.edu/sarcasm2
Sarcasm Amazon Reviews Corpushttps://github.com/ef2020/SarcasmAmazonReviewsCorpus
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-grounding
Symbol grounding problemhttps://en.wikipedia.org/wiki/Symbol_grounding_problem
The Symbol Grounding Problemhttp://courses.media.mit.edu/2004spring/mas966/Harnad%20symbol%20grounding.pdf
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learninghttps://arxiv.org/abs/1610.03342
Encoding of phonology in a recurrent neural model of grounded speechhttps://arxiv.org/abs/1706.03815
Gated-Attention Architectures for Task-Oriented Language Groundinghttps://arxiv.org/abs/1706.07230
Sound-Word2Vec: Learning Word Representations Grounded in Soundshttps://arxiv.org/abs/1703.01720
Language Grounding to Vision and Controlhttps://www.cs.cmu.edu/~katef/808/
Language Grounding for Roboticshttps://robonlp2017.github.io/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-guessing
Language Identificationhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-identification
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-identification
Language identificationhttps://en.wikipedia.org/wiki/Language_identification
AUTOMATIC LANGUAGE IDENTIFICATION USING DEEP NEURAL NETWORKShttps://repositorio.uam.es/bitstream/handle/10486/666848/automatic_lopez-moreno_ICASSP_2014_ps.pdf?sequence=1
Natural Language Processing with Small Feed-Forward Networkshttp://aclweb.org/anthology/D17-1308
2015 Language Recognition Evaluationhttps://www.nist.gov/itl/iad/mig/2015-language-recognition-evaluation
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-modeling
Language modelhttps://en.wikipedia.org/wiki/Language_model
KenLM Language Model Toolkithttp://kheafield.com/code/kenlm/
Distributed Representations of Words and Phrases and their Compositionalityhttp://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
Generating Sequences with Recurrent Neural Networkshttps://arxiv.org/pdf/1308.0850.pdf
Character-Aware Neural Language Modelshttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12489/12017
Statistical Language Models Based on Neural Networkshttp://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf
Penn Treebankhttps://github.com/townie/PTB-dataset-from-Tomas-Mikolov-s-webpage/tree/master/data
TensorFlow Tutorial on Language Modeling with Recurrent Neural Networkshttps://www.tensorflow.org/tutorials/recurrent#language_modeling
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-recognition
Language Identificationhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#language-identification
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#lemmatisation
Lemmatisationhttps://en.wikipedia.org/wiki/Lemmatisation
Joint Lemmatization and Morphological Tagging with LEMMINGhttp://www.cis.lmu.de/~muellets/pdf/emnlp_2015.pdf
WordNet Lemmatizerhttp://www.nltk.org/api/nltk.stem.html#nltk.stem.wordnet.WordNetLemmatizer.lemmatize
Treebank-3https://catalog.ldc.upenn.edu/ldc99t42
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#lip-reading
Lip readinghttps://en.wikipedia.org/wiki/Lip_reading
Lip Reading Sentences in the Wildhttps://arxiv.org/abs/1611.05358
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognitionhttps://arxiv.org/abs/1706.05739
Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networkshttps://github.com/astorfi/lip-reading-deeplearning
The GRID audiovisual sentence corpushttp://spandh.dcs.shef.ac.uk/gridcorpus/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#machine-translation
Neural Machine Translation by Jointly Learning to Align and Translatehttps://arxiv.org/abs/1409.0473
Neural Machine Translation in Linear Timehttps://arxiv.org/abs/1610.10099
Attention Is All You Needhttps://arxiv.org/abs/1706.03762
Six Challenges for Neural Machine Translationhttp://aclweb.org/anthology/W/W17/W17-3204.pdf
ACL 2014 NINTH WORKSHOP ON STATISTICAL MACHINE TRANSLATIONhttp://www.statmt.org/wmt14/translation-task.html#download
EMNLP 2017 SECOND CONFERENCE ON MACHINE TRANSLATION (WMT17) http://www.statmt.org/wmt17/translation-task.html
OpenSubtitles2016http://opus.lingfil.uu.se/OpenSubtitles2016.php
WIT3: Web Inventory of Transcribed and Translated Talkshttps://wit3.fbk.eu/
The QCRI Educational Domain (QED) Corpushttp://alt.qcri.org/resources/qedcorpus/
Multi-task Sequence to Sequence Learninghttps://arxiv.org/abs/1511.06114
Unsupervised Pretraining for Sequence to Sequence Learninghttp://aclweb.org/anthology/D17-1039
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationhttps://arxiv.org/abs/1611.04558
Subword Neural Machine Translation with Byte Pair Encoding (BPE)https://github.com/rsennrich/subword-nmt
Multi-Way Neural Machine Translationhttps://github.com/nyu-dl/dl4mt-multi
OpenNMT: Open-Source Toolkit for Neural Machine Translationhttp://opennmt.net/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#morphological-inflection-generation
Inflectionhttps://en.wikipedia.org/wiki/Inflection
Morphological Inflection Generation Using Character Sequence to Sequence Learninghttps://arxiv.org/abs/1512.06110
SIGMORPHON 2016 Shared Task: Morphological Reinflectionhttp://ryancotterell.github.io/sigmorphon2016/
sigmorphon2016https://github.com/ryancotterell/sigmorphon2016
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#named-entity-disambiguation
Entity linkinghttps://en.wikipedia.org/wiki/Entity_linking
Robust and Collective Entity Disambiguation through Semantic Embeddingshttp://www.stefanzwicklbauer.info/pdf/Sigir_2016.pdf
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#named-entity-recognition
Named-entity recognitionhttps://en.wikipedia.org/wiki/Named-entity_recognition
Neural Architectures for Named Entity Recognitionhttps://arxiv.org/abs/1603.01360
OSU Twitter NLP Toolshttps://github.com/aritter/twitter_nlp
Named Entity Recognition in Twitterhttps://noisy-text.github.io/2016/ner-shared-task.html
CoNLL 2002 Language-Independent Named Entity Recognitionhttps://www.clips.uantwerpen.be/conll2002/ner/
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognitionhttp://aclweb.org/anthology/W03-0419
CoNLL-2002 NER corpushttps://github.com/teropa/nlp/tree/master/resources/corpora/conll2002
CoNLL-2003 NER corpushttps://github.com/synalp/NER/tree/master/corpus/CoNLL-2003
NUT Named Entity Recognition in Twitter Shared taskhttps://github.com/aritter/twitter_nlp/tree/master/data/annotated/wnut16
Stanford Named Entity Recognizerhttps://nlp.stanford.edu/software/CRF-NER.shtml
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#paraphrase-detection
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detectionhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.650.7199&rep=rep1&type=pdf
Paralex: Paraphrase-Driven Learning for Open Question Answeringhttp://knowitall.cs.washington.edu/paralex/
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitterhttp://alt.qcri.org/semeval2015/task1/
Microsoft Research Paraphrase Corpushttps://www.microsoft.com/en-us/download/details.aspx?id=52398
Microsoft Research Video Description Corpushttps://www.microsoft.com/en-us/download/details.aspx?id=52422&from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fdownloads%2F38cf15fd-b8df-477e-a4e4-a4680caa75af%2F
Pascal Datasethttp://nlp.cs.illinois.edu/HockenmaierGroup/pascal-sentences/index.html
Flickr Datasethttp://nlp.cs.illinois.edu/HockenmaierGroup/8k-pictures.html
The SICK data sethttp://clic.cimec.unitn.it/composes/sick.html
PPDB: The Paraphrase Databasehttp://www.cis.upenn.edu/%7Eccb/ppdb/
WikiAnswers Paraphrase Corpushttp://knowitall.cs.washington.edu/paralex/wikianswers-paraphrases-1.0.tar.gz
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#paraphrase-generation
Neural Paraphrase Generation with Stacked Residual LSTM Networkshttps://arxiv.org/pdf/1610.03098.pdf
Neural Paraphrase Generation with Stacked Residual LSTM Networkshttps://github.com/iamaaditya/neural-paraphrase-generation/tree/master/data
Neural Paraphrase Generation with Stacked Residual LSTM Networkshttps://github.com/iamaaditya/neural-paraphrase-generation
A Deep Generative Framework for Paraphrase Generationhttps://arxiv.org/pdf/1709.05074.pdf
Paraphrasing Revisited with Neural Machine Translationhttp://www.research.ed.ac.uk/portal/files/34902784/document.pdf
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#parsing
Parsinghttps://en.wikipedia.org/wiki/Parsing
The Stanford Parser: A statistical parserhttps://nlp.stanford.edu/software/lex-parser.shtml
spaCy parserhttps://spacy.io/docs/usage/dependency-parse
Grammar as a Foreign Languagehttps://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf
A fast and accurate dependency parser using neural networkshttp://www.aclweb.org/anthology/D14-1082
Universal Semantic Parsinghttps://aclanthology.info/pdf/D/D17/D17-1009.pdf
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencieshttp://universaldependencies.org/conll17/
CoNLL 2016 Shared Task: Multilingual Shallow Discourse Parsinghttp://www.cs.brandeis.edu/~clp/conll16st/
CoNLL 2015 Shared Task: Shallow Discourse Parsinghttp://www.cs.brandeis.edu/~clp/conll15st/
SemEval-2016 Task 8: The meaning representations may be abstract, but this task is concrete!http://alt.qcri.org/semeval2016/task8/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#part-of-speech-tagging
Part-of-speech tagginghttps://en.wikipedia.org/wiki/Part-of-speech_tagging
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Losshttps://arxiv.org/pdf/1604.05529.pdf
Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Modelshttps://transacl.org/ojs/index.php/tacl/article/viewFile/837/192
Treebank-3https://catalog.ldc.upenn.edu/ldc99t42
nltk.tag packagehttp://www.nltk.org/api/nltk.tag.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#pinyin-to-chinese-conversion
Pinyin input methodhttps://en.wikipedia.org/wiki/Pinyin_input_method
Neural Network Language Model for Chinese Pinyin Input Method Enginehttp://aclweb.org/anthology/Y15-1052
Neural Chinese Transliteratorhttps://github.com/Kyubyong/neural_chinese_transliterator
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#question-answering
Question answeringhttps://en.wikipedia.org/wiki/Question_answering
Ask Me Anything: Dynamic Memory Networks for Natural Language Processinghttp://www.thespermwhale.com/jaseweston/ram/papers/paper_21.pdf
Dynamic Memory Networks for Visual and Textual Question Answeringhttp://proceedings.mlr.press/v48/xiong16.pdf
TREC Question Answering Taskhttp://trec.nist.gov/data/qamain.html
NTCIR-8: Advanced Cross-lingual Information Access (ACLIA)http://aclia.lti.cs.cmu.edu/ntcir8/Home
CLEF Question Answering Trackhttp://nlp.uned.es/clef-qa/
SemEval-2017 Task 3: Community Question Answeringhttp://alt.qcri.org/semeval2017/task3/
SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge (In Progress)https://competitions.codalab.org/competitions/17184
MS MARCO: Microsoft MAchine Reading COmprehension Datasethttp://www.msmarco.org/
Maluuba NewsQAhttps://github.com/Maluuba/newsqa
SQuAD: 100,000+ Questions for Machine Comprehension of Texthttps://rajpurkar.github.io/SQuAD-explorer/
GraphQuestions: A Characteristic-rich Question Answering Datasethttps://github.com/ysu1989/GraphQuestions
Story Cloze Test and ROCStories Corporahttp://cs.rochester.edu/nlp/rocstories/
Microsoft Research WikiQA Corpushttps://www.microsoft.com/en-us/download/details.aspx?id=52419&from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fdownloads%2F4495da01-db8c-4041-a7f6-7984a4f6a905%2Fdefault.aspx
DeepMind Q&A Datasethttp://cs.nyu.edu/%7Ekcho/DMQA/
QASenthttp://cs.stanford.edu/people/mengqiu/data/qg-emnlp07-data.tgz
Textbook Question Answeringhttp://textbookqa.org/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#relationship-extraction
Relationship extractionhttps://en.wikipedia.org/wiki/Relationship_extraction
A deep learning approach for relationship extraction from interaction context in social manufacturing paradigmhttp://www.sciencedirect.com/science/article/pii/S0950705116001210
SemEval-2018 task 7 Semantic Relation Extraction and Classification in Scientific Papers (In Progress)https://competitions.codalab.org/competitions/17422
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#semantic-role-labeling
Semantic role labelinghttps://en.wikipedia.org/wiki/Semantic_role_labeling
Semantic Role Labelinghttps://www.amazon.com/Semantic-Labeling-Synthesis-Lectures-Technologies/dp/1598298313/ref=sr_1_1?s=books&ie=UTF8&qid=1507776173&sr=1-1&keywords=Semantic+Role+Labeling
End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networkshttp://www.aclweb.org/anthology/P/P15/P15-1109.pdf
Neural Semantic Role Labeling with Dependency Path Embeddingshttps://arxiv.org/abs/1605.07515
Deep Semantic Role Labeling: What Works and What's Nexthttps://homes.cs.washington.edu/~luheng/files/acl2017_hllz.pdf
CoNLL-2005 Shared Task: Semantic Role Labelinghttp://www.cs.upc.edu/~srlconll/st05/st05.html
CoNLL-2004 Shared Task: Semantic Role Labelinghttp://www.cs.upc.edu/~srlconll/st04/st04.html
Illinois Semantic Role Labeler (SRL)http://cogcomp.org/page/software_view/SRL
CoNLL-2005 Shared Task: Semantic Role Labelinghttp://www.cs.upc.edu/~srlconll/soft.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#sentence-boundary-disambiguation
Sentence boundary disambiguationhttps://en.wikipedia.org/wiki/Sentence_boundary_disambiguation
A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domainhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001746/
NLTK Tokenizershttp://www.nltk.org/_modules/nltk/tokenize.html
The British National Corpushttp://www.natcorp.ox.ac.uk/
Switchboard-1 Telephone Speech Corpushttps://catalog.ldc.upenn.edu/ldc97s62
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#sentiment-analysis
Sentiment analysishttps://en.wikipedia.org/wiki/Sentiment_analysis
Awesome Sentiment Analysishttps://github.com/xiamx/awesome-sentiment-analysis
Kaggle: UMICH SI650 - Sentiment Classificationhttps://www.kaggle.com/c/si650winter11#description
SemEval-2017 Task 4: Sentiment Analysis in Twitterhttp://alt.qcri.org/semeval2017/task4/
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and Newshttp://alt.qcri.org/semeval2017/task5/
SenticNethttp://sentic.net/about/
Stanford NLP Group Sentiment Analysishttps://nlp.stanford.edu/sentiment/
Multi-Domain Sentiment Dataset (version 2.0)http://www.cs.jhu.edu/%7Emdredze/datasets/sentiment/
Stanford Sentiment Treebankhttps://nlp.stanford.edu/sentiment/code.html
Twitter Sentiment Corpushttp://www.sananalytics.com/lab/twitter-sentiment/
Twitter Sentiment Analysis Training Corpushttp://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/
AFINN: List of English words rated for valencehttp://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#singing-voice-synthesis
Singing voice synthesis based on deep neural networkshttps://pdfs.semanticscholar.org/9a8e/b69480eead85f32ee4b92fa2563dd5f83401.pdf
A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songshttp://www.mdpi.com/2076-3417/7/12/1313
VOCALOID: voice synthesis technology and software developed by Yamahahttps://www.vocaloid.com/en
Special Session Interspeech 2016 Singing synthesis challenge "Fill-in the Gap"https://chanter.limsi.fr/doku.php?id=description:start
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#social-science-applications
NLP+CSS: Workshops on Natural Language Processing and Computational Social Sciencehttps://sites.google.com/site/nlpandcss/
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintshttps://github.com/uclanlp/reducingbias
Online Variational Bayes for Latent Dirichlet Allocation (LDA)https://github.com/blei-lab/onlineldavb
The University of Chicago Knowledge Labhttp://www.knowledgelab.org/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#source-separation
Source separationhttps://en.wikipedia.org/wiki/Source_separation
From Blind to Guided Audio Source Separationhttps://hal-univ-rennes1.archives-ouvertes.fr/hal-00922378/document
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separationhttps://arxiv.org/abs/1502.04149
Signal Separation Evaluation Campaign (SiSEC)https://sisec.inria.fr/
CHiME Speech Separation and Recognition Challengehttp://spandh.dcs.shef.ac.uk/chime_challenge/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speaker-authentication
Speaker Verificationhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speaker-verification
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speaker-diarisation
Speaker diarisationhttps://en.wikipedia.org/wiki/Speaker_diarisation
DNN-based speaker clustering for speaker diarisationhttp://eprints.whiterose.ac.uk/109281/1/milner_is16.pdf
Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approachhttp://groups.csail.mit.edu/sls/publications/2013/Shum_IEEE_Oct-2013.pdf
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusionhttps://arxiv.org/pdf/1603.09725.pdf
Rich Transcription Evaluationhttps://www.nist.gov/itl/iad/mig/rich-transcription-evaluation
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speaker-recognition
Speaker recognitionhttps://en.wikipedia.org/wiki/Speaker_recognition
A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORKhttps://pdfs.semanticscholar.org/204a/ff8e21791c0a4113a3f75d0e6424a003c321.pdf
DEEP NEURAL NETWORKS FOR SMALL FOOTPRINT TEXT-DEPENDENT SPEAKER VERIFICATIONhttps://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41939.pdf
NIST Speaker Recognition Evaluation (SRE)https://www.nist.gov/itl/iad/mig/speaker-recognition
Are there any suggestions for free databases for speaker recognition?https://www.researchgate.net/post/Are_there_any_suggestions_for_free_databases_for_speaker_recognition
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-reading
Lip-readinghttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#lip-reading
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-recognition
Automatic Speech Recognitionhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#automatic-speech-recognition
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-segmentation
Speech_segmentationhttps://en.wikipedia.org/wiki/Speech_segmentation
Word Segmentation by 8-Month-Olds: When Speech Cues Count More Than Statisticshttp://www.utm.toronto.edu/infant-child-centre/sites/files/infant-child-centre/public/shared/elizabeth-johnson/Johnson_Jusczyk.pdf
Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddingshttps://arxiv.org/abs/1603.02845
Unsupervised Lexicon Discovery from Acoustic Inputhttp://www.aclweb.org/old_anthology/Q/Q15/Q15-1028.pdf
Weakly supervised spoken term discovery using cross-lingual side informationhttp://www.research.ed.ac.uk/portal/files/29957958/1609.06530v1.pdf
CALLHOME Spanish Speechhttps://catalog.ldc.upenn.edu/ldc96s35
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-synthesis
Speech synthesishttps://en.wikipedia.org/wiki/Speech_synthesis
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionshttps://arxiv.org/abs/1712.05884
WaveNet: A Generative Model for Raw Audiohttps://arxiv.org/abs/1609.03499
Tacotron: Towards End-to-End Speech Synthesishttps://arxiv.org/abs/1703.10135
Deep Voice 3: 2000-Speaker Neural Text-to-Speechhttps://arxiv.org/abs/1710.07654
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attentionhttps://arxiv.org/abs/1710.08969
The World English Biblehttps://github.com/Kyubyong/tacotron
LJ Speech Datasethttps://github.com/keithito/tacotron
Lessac Datahttp://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/
Blizzard Challenge 2017https://synsig.org/index.php/Blizzard_Challenge_2017
Lyrebirdhttps://lyrebird.ai/
The Festvox projecthttp://www.festvox.org/index.html
Merlin: The Neural Network (NN) based Speech Synthesis Systemhttps://github.com/CSTR-Edinburgh/merlin
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-enhancement
Speech enhancementhttps://en.wikipedia.org/wiki/Speech_enhancement
Speech enhancement: theory and practicehttps://www.amazon.com/Speech-Enhancement-Theory-Practice-Second/dp/1466504218/ref=sr_1_1?ie=UTF8&qid=1507874199&sr=8-1&keywords=Speech+enhancement%3A+theory+and+practice
An Experimental Study on Speech Enhancement BasedonDeepNeuralNetworkhttp://staff.ustc.edu.cn/~jundu/Speech%20signal%20processing/publications/SPL2014_Xu.pdf
A Regression Approach to Speech Enhancement BasedonDeepNeuralNetworkshttps://www.researchgate.net/profile/Yong_Xu63/publication/272436458_A_Regression_Approach_to_Speech_Enhancement_Based_on_Deep_Neural_Networks/links/57fdfdda08aeaf819a5bdd97.pdf
Speech Enhancement Based on Deep Denoising Autoencoderhttps://www.researchgate.net/profile/Yu_Tsao/publication/283600839_Speech_enhancement_based_on_deep_denoising_Auto-Encoder/links/577b486108ae213761c9c7f8/Speech-enhancement-based-on-deep-denoising-Auto-Encoder.pdf
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-to-text
Automatic Speech Recognitionhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#automatic-speech-recognition
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#spoken-term-detection
Speech Segmentationhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-segmentation
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#stemming
Stemminghttps://en.wikipedia.org/wiki/Stemming
A BACKPROPAGATION NEURAL NETWORK TO IMPROVE ARABIC STEMMINGhttp://www.jatit.org/volumes/Vol82No3/7Vol82No3.pdf
NLTK Stemmershttp://www.nltk.org/howto/stem.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#term-extraction
Terminology extractionhttps://en.wikipedia.org/wiki/Terminology_extraction
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detectionhttps://arxiv.org/pdf/1604.00077.pdf
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#text-similarity
Semantic similarityhttps://en.wikipedia.org/wiki/Semantic_similarity
A Survey of Text Similarity Approacheshttps://pdfs.semanticscholar.org/5b5c/a878c534aee3882a038ef9e82f46e102131b.pdf
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networkshttp://casa.disi.unitn.it/~moschitt/since2013/2015_SIGIR_Severyn_LearningRankShort.pdf
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkshttps://nlp.stanford.edu/pubs/tai-socher-manning-acl2015.pdf
SemEval-2014 Task 3: Cross-Level Semantic Similarityhttp://alt.qcri.org/semeval2014/task3/
SemEval-2014 Task 10: Multilingual Semantic Textual Similarityhttp://alt.qcri.org/semeval2014/task10/
SemEval-2017 Task 1: Semantic Textual Similarityhttp://alt.qcri.org/semeval2017/task1/
Semantic Textual Similarity Wikihttp://ixa2.si.ehu.es/stswiki/index.php/Main_Page
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#text-simplification
Text simplificationhttps://en.wikipedia.org/wiki/Text_simplification
Aligning Sentences from Standard Wikipedia to Simple Wikipediahttps://ssli.ee.washington.edu/~hannaneh/papers/simplification.pdf
Problems in Current Text Simplification Research: New Data Can Helphttps://pdfs.semanticscholar.org/2b8d/a013966c0c5e020ebc842d49d8ed166c8783.pdf
Newsela Datahttps://newsela.com/data/
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#text-to-speech
Speech Synthesishttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speech-synthesis
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#textual-entailment
Textual entailmenthttps://en.wikipedia.org/wiki/Textual_entailment
Textual Entailment with TensorFlowhttps://github.com/Steven-Hewitt/Entailment-with-Tensorflow
Textual Entailment with Structured Attentions and Compositionhttps://arxiv.org/pdf/1701.01126.pdf
SemEval-2014 Task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailmenthttp://alt.qcri.org/semeval2014/task1/
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challengehttps://www.cs.york.ac.uk/semeval-2013/task7.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#transliteration
Transliterationhttps://en.wikipedia.org/wiki/Transliteration
Transliteration of Non-Latin scriptshttp://transliteration.eki.ee/
A Deep Learning Approach to Machine Transliterationhttps://pdfs.semanticscholar.org/54f1/23122b8dd1f1d3067cf348cfea1276914377.pdf
NEWS 2016 Shared Task on Transliteration of Named Entitieshttp://workshop.colips.org/news2016/index.html
Neural Japanese Transliteration—can you do better than SwiftKey™ Keyboard?https://github.com/Kyubyong/neural_japanese_transliterator
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#voice-conversion
PHONETIC POSTERIORGRAMS FOR MANY-TO-ONE VOICE CONVERSION WITHOUT PARALLEL DATA TRAININGhttp://www1.se.cuhk.edu.hk/~hccl/publications/pub/2016_paper_297.pdf
Deep neural networks for voice conversion (voice style transfer) in Tensorflowhttps://github.com/andabi/deep-voice-conversion
An implementation of voice conversion system utilizing phonetic posteriorgramshttps://github.com/sesenosannko/ppg_vc
Voice Conversion Challenge 2016http://www.vc-challenge.org/vcc2016/index.html
Voice Conversion Challenge 2018http://www.vc-challenge.org/
CMU_ARCTIC speech synthesis databaseshttp://festvox.org/cmu_arctic/
TIMIT Acoustic-Phonetic Continuous Speech Corpushttps://catalog.ldc.upenn.edu/ldc93s1
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#voice-recognition
Speaker recognitionhttps://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#speaker-recognition
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#word-embeddings
Word embeddinghttps://en.wikipedia.org/wiki/Word_embedding
Gensim: word2vechttps://radimrehurek.com/gensim/models/word2vec.html
fastTexthttps://github.com/facebookresearch/fastText
GloVe: Global Vectors for Word Representationhttps://nlp.stanford.edu/projects/glove/
Where to get a pretrained modelhttps://github.com/3Top/word2vec-api
Pre-trained word vectors of 30+ languageshttps://github.com/Kyubyong/wordvectors
Polyglot: Distributed word representations for multilingual NLPhttps://sites.google.com/site/rmyeid/projects/polyglot
SemEval 2018 Task 10 Capturing Discriminative Attributes (In Progress)https://competitions.codalab.org/competitions/17326
Bilingual Word Embeddings for Phrase-Based Machine Translationhttps://ai.stanford.edu/~wzou/emnlp2013_ZouSocherCerManning.pdf
A Survey of Cross-Lingual Embedding Modelshttps://arxiv.org/abs/1706.04902
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#word-prediction
What is Word Prediction?http://www2.edc.org/ncip/library/wp/what_is.htm
The prediction of character based on recurrent neural network language modelhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7960065
An Embedded Deep Learning based Word Predictionhttps://arxiv.org/abs/1707.01662
Evaluating Word Prediction: Framing Keystroke Savingshttp://aclweb.org/anthology/P08-2066
An Embedded Deep Learning based Word Predictionhttps://github.com/Meinwerk/WordPrediction/master.zip
Word Prediction using Convolutional Neural Networks—can you do better than iPhone™ Keyboard?https://github.com/Kyubyong/word_prediction
SemEval-2018 Task 2, Multilingual Emoji Prediction (In Progress)https://competitions.codalab.org/competitions/17344
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#word-segmentation
Word segmentationhttps://en.wikipedia.org/wiki/Text_segmentation#Segmentation_problems
Neural Word Segmentation Learning for Chinesehttps://arxiv.org/abs/1606.04300
Convolutional neural network for Chinese word segmentationhttps://github.com/chqiwang/convseg
Stanford Word Segmenterhttps://nlp.stanford.edu/software/segmenter.html
NLTK Tokenizershttp://www.nltk.org/_modules/nltk/tokenize.html
https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#word-sense-disambiguation
Word-sense disambiguationhttps://en.wikipedia.org/wiki/Word-sense_disambiguation
Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Datahttp://www.aclweb.org/anthology/D17-1008
Train-O-Matic Datahttp://trainomatic.org/data/train-o-matic-data.zip
BabelNethttp://babelnet.org/
Readme https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#readme-ov-file
Apache-2.0 license https://github.com/PhantomGM/https-github.com-Satssuki-nlp_tasks#Apache-2.0-1-ov-file
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