Difference between revisions of "BA/app"
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[http://www.boente.eti.br/fuzzy/ebook-fuzzy-mitchell.pdf An introduction to 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 === | === Text classification === | ||
| Line 13: | Line 15: | ||
With machine learning: | 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://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] | + | * [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] | + | * [http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html Working with scikit and text data] |
=== Error Detection === | === Error Detection === | ||
| Line 24: | Line 26: | ||
[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://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://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://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. | + | * [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. |
| − | |||
| − | |||
== Random == | == Random == | ||
[https://www.safaribooksonline.com/library/view/natural-language-annotation/9781449332693/ Natural Language Annotation for Machine Learning] ebook, seems to cover quite a lot | [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 -- incl datasets for CL lying detection -- generally interesting | [http://lit.eecs.umich.edu/downloads.html#Cross-Cultural%20Deception downloads and demos -- incl datasets for CL lying detection -- generally interesting | ||
Revision as of 14:03, 26 November 2017
Contents
Primary sources
Computer linguistics: CL intro
Genetic Algorithms: An introduction to genetic algorithms
Linguistics: | 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:Book, especially learning to classify text
With machine learning:
- with tensorflow and generally nns: [1]
- Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK
- Working with scikit and text data
Error Detection
error detection using local word bigram and trigram + some others
Somewhat similar problems being solved
Cross-cultural Deception Detection. It uses unigrams + LIWC (which is more psychological and less relevant)
- Deception detection -- has examples of extracted features which I might use
- [2] -- lie detector
- Linguistic Cues to Deception Assessed by Computer Programs: A Meta-Analysis -- also ideas of possible features that might be interesting to look into.
Random
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 -- incl datasets for CL lying detection -- generally interesting
