View source for BA/app

== Primary sources ==
Computer linguistics:
[http://cs.brown.edu/courses/csci2951-k/papers/cl-intro.pdf CL intro]

Genetic Algorithms:
[http://www.boente.eti.br/fuzzy/ebook-fuzzy-mitchell.pdf An introduction to genetic algorithms]

Linguistics: 
[https://is.muni.cz/th/180075/ff_b/Thesis_2nd_draft.txt Dissertation partly about interferences]. Has a nice error classification, error taxonomy, borrowing, tranfer etc etc. Seems like a nice intro to "What exists"

== CL/ML resources ==
=== Text classification ===

Natural language classification with Python:[http://www.nltk.org/book/ Book], especially [http://www.nltk.org/book/ch06.html learning to classify text]

With machine learning:
* [https://medium.freecodecamp.org/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274 with tensorflow] and generally nns: [https://machinelearnings.co/text-classification-using-neural-networks-f5cd7b8765c6]
* [https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK]
* [http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html Working with scikit and text data]

=== Error Detection ===

[http://www.aclweb.org/anthology/O13-1022 error detection using local word bigram and trigram] + some others
[http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00072 Automatic error analysis of machine translation output] -- more about possible errors and ways to classify them

=== Somewhat similar problems being solved ===
[http://www.anthology.aclweb.org/P/P14/P14-2072.pdf Cross-cultural Deception Detection]. It uses unigrams + LIWC (which is more psychological and less relevant)

* [http://aclweb.org/anthology/D/D15/D15-1133.pdf Deception detection] -- has examples of extracted features which I might use
* [http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.acl09.pdf] -- lie detector
* [http://delivery.acm.org/10.1145/2390000/2388617/p1-hauch.pdf?ip=149.205.109.95&id=2388617&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=1008304166&CFTOKEN=69973089&__acm__=1511275273_f72fd72f6e2433e82566681fc1a564cb Linguistic Cues to Deception Assessed by Computer Programs: A Meta-Analysis] -- also ideas of possible features that might be interesting to look into.

== Linguistics  ==
=== Typical errors ===
==== Russian ====
*[http://www.simonf.com/lang/mistakes_russian_win.html Similar-sounding and semantically non-identical words + idioms]
* ''[http://www.study.ru/support/lib/note281.html Grammar]. Articles, connecting verbs, future tenss, negative sentences, commas etc etc -- really nice.''
==== German ====
* [https://www.englishwithnick.de/resources-for-germans/typical-grammar-mistakes-made-by-germans/ list of sentences]
* [http://londonschool.de/top-english-mistakes-made-german-learners-volume-1/ also examples, hard to generalize]
* [https://englishwithkirsty.com/2014/07/15/10-typical-mistakes-made-by-german-speakers-who-are-learning-english/ examples, a bit better ones?]
* [http://www.jabbalab.com/blog/966/how-and-when-to-use-german-reflexive-verbs German reflexive verbs list] which could be used to see differences between English and German reflexive verbs.

==== Indian ====
* [https://en.wikipedia.org/wiki/Indian_English#Morphology_and_syntax Wikipedia - Indian English] I thing this could be done just statistically? 

==== Italian ====
???

== Random ==
[https://www.safaribooksonline.com/library/view/natural-language-annotation/9781449332693/ Natural Language Annotation for Machine Learning] ebook, seems to cover quite a lot

[http://lit.eecs.umich.edu/downloads.html#Cross-Cultural%20Deception downloads and demos -- datasets for CL lying detection] -- generally interesting
 
[https://www.uclassify.com/browse/uclassify/ Classification-as-a-service with free examples]. Gender, MBTI, etc etc etc, pretty nice