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NLP Reading List 202017

-- Conformal Prediction Tool https://github.com/donlnz/nonconformist -- Ensemble Methods in ML http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf -- Q&A in Restriced Domains http://www.mitpressjournals.org/doi/pdf/10.1162/coli.2007.33.1.41 https://web.stanford.edu/~jurafsky/slp3/28.pdf

-- Prediction Server https://github.com/ucbrise/clipper

-- Reinforcement Learning https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf

-- sklearn Model to Java https://github.com/nok/sklearn-porter

-- Open Source Language Understanding https://rasa.ai

-- Emotion Recognition https://github.com/HashCode55/Emotion-Recognition

-- General Purpose Object Detection https://github.com/EvgenyNekrasov/gpod

-- Relief-based feature selection https://github.com/EpistasisLab/scikit-rebate

-- Bio Medical Data https://github.com/EpistasisLab/ClinicalDataSources

-- Multifactor Dimensionality Reduction https://github.com/EpistasisLab/scikit-mdr

-- Spira Python receommener https://github.com/mblondel/spira

-- Massive Online Dictionary Learning https://github.com/arthurmensch/modl

-- Declarative Learning-Based Programming https://github.com/IllinoisCogComp/saul

-- Many Languages, One Parser https://arxiv.org/abs/1602.01595

-- Pynini Google FST Parser http://openfst.cs.nyu.edu/twiki/pub/GRM/Pynini/pynini-paper.pdf

-- Reproducible Data Science https://github.com/smorante/machine-learning-knowledge-pills/blob/master/reproducible%20data%20science/reproducible-data-science.md

-- Survey on Real Time Bidding Advertising http://wnzhang.net/share/rtb-papers/rtb-survey.pdf

-- Experimental Data for Question Classification http://cogcomp.cs.illinois.edu/Data/QA/QC/

-- Biterm Topic Model https://github.com/jcapde/Biterm

-- Open Source Nural MT http://opennmt.net/OpenNMT/

-- Kohan Vector Quantizer https://github.com/lmjohns3/kohonen

-- NLP Deep Learning Code https://github.com/RubensZimbres/Repo-2017

-- Clustering with Confidence: https://arxiv.org/pdf/1612.08714.pdf

-- Python Data Science Handbook https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/README.md

-- “Why Should I Trust you?” Explaining the Predictions of Any Classifier http://www.kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-explaining-the-predictions-of-any-classifier

-- “Why Should I Trust you?” https://github.com/marcotcr/lime

-- “Why Should I Trust you?” http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf

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