This document discusses various approaches to question answering systems, including rule-based systems, bag-of-words models, word embeddings, convolutional neural networks, recurrent neural networks, and main challenges. It covers natural language processing techniques, different QA standards, and provides links to resources on related topics such as word embeddings and Russian language parsing.

1. QnA bot. Experience

2. http://www.slideshare.net/YuriyGuts/natural-language-processing-nlp
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3. Standards in QA systems
Rule-based systems
https://www.researchgate.net/publication/235923461_QArabPro_A_Rule_Based_Question_Answering_
System_for_Reading_Comprehension_Tests_in_Arabic
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4. Standards in QA systems
Bag of words
http://www.slideshare.net/hadyelsahar/word-embedings-why-the-hype-55769273
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5. Bag of words + TF-IDF
http://www.slideshare.net/hadyelsahar/word-embedings-why-the-hype-55769273
Standards in QA systems
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6. Word embedding
https://blog.heuritech.com/2016/04/11/report-of-the-heuritech-deep-learning-meetup-6-with-yann-lecun/
Standards in QA systems
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7. 1D Convolutional neural network
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
Standards in QA systems
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8. Recurrent Neural Network
Standards in QA systems
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
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9. Standards in QA systems
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10. Main problems
1) Limited data
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11. Main problems
2) Difference between train dataset and “real life”
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12. Main problems
3) “I don't know” answers
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13. Main problems
4) Quality metrics
https://arxiv.org/pdf/1603.08023v2.pdf
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14. Main problems
5) Model degradation and “refitting model”
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15. Main problems
6) Trash questions
Where is the nearest library?
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16. One more thing...
Russian language parser
Word embedding API (word2vec, Glove)
Solutions for business
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17. Наши
кейсы
From Russia with love