This document contains the slides for a presentation on natural language processing (NLP) with .NET. The presentation introduces common NLP tasks like analysis, transformation, and generation. It discusses NLP concepts like bag-of-words, TF-IDF, n-grams, and word embeddings. Tools for NLP with .NET are presented, including ML.NET, Catalyst, and Microsoft Recognizers libraries. Demonstrations of text summarization and document tagging using these tools are described. The presentation concludes that NLP for basic tasks is possible with .NET libraries, though features are still limited compared to other languages.

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KYIV 2019
Natural Language Processing with .NET
.NET CONFERENCE #1 IN UKRAINE

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.NET LEVEL UP
About me
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Sergiy Korzh
25+ years in software development
20 year running own business
.NET developer since 2004
iForum.ua (technology section)
Projects:
EasyQuery (https://korzh.com/easyquery)
Easy.Report (http://easy.report)
Aistant (https://aistant.com/)
Twitter: @korzhs
LinkedIn: https://www.linkedin.com/in/korzh/

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.NET LEVEL UP
Agenda
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
1 Introduction to NLP (main tasks and basic concepts)
NLP Tools for .NET (and not only)2
3 Demos
4 Useful materials and conclusions

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.NET LEVEL UP
Why NLP on .NET?
.NET CONFERENCE #1 IN UKRAINE KYIV 2019

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.NET LEVEL UP
Why NLP on .NET?
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Because we love .NET, right?
Quick and easy (for simple NLP tasks)
No “glue” code

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Remarks
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
“Light” NLP tasks only!
No Deep Learning
Beginner level topics

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NLP Tasks
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
1 Linguistic
Analysis
Transformation
2
3
Generation4

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NLP Tasks
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
1 Linguistic
• Segmentation
• Part of speech tagging
• Named-entity recognition
• Relation extraction
• Syntactic parsing
• Coreference resolution
• Semantic parsing

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NLP Tasks’ Examples
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
2 Analysis
• Spam-filter
• Sentiment analysis
• Text similarity
• Information extraction

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NLP Tasks’ Examples
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
3 Transformation
• Machine translation
• Speech to Text / Text to speech
• Grammar correction
• Text summarization

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NLP Tasks’ Examples
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
4 Generation
• Question Answering
• Chat bots
• Story generation

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NLP Pipeline
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
TEXT Text Featurizing
(Numeric representation)
ML Algorithm RESULT

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NLP Pipeline: Classic
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
from AYLIEN blog

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NLP Pipeline: Deep Learning
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
from AYLIEN blog

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NLP concepts: Bag of words
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
The way to represent your text for ML algorithms
• Word frequency
• One-hot encoding
• TF-IDF
• Other metrics
Encoding approaches:

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NLP concepts: TF-IDF
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
For a word-document pair, TF-IDF shows the
importance of the word in the document.
Used in all kinds of information retrieval tasks:
• Search
• Text mining
• Stop-words filtering

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NLP concepts: N-grams
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Word N-grams
n-gram is a contiguous sequence of n items from a given sample of text.
“I live in Kyiv” word bi-grams
1. # I
2. I live
3. live in
4. in Kyiv
5. Kyiv #
Character N-grams
“I live in Kyiv” character bi-grams
1. #_
2. _I
3. I_
4. _l
5. li
6. Iv
7. ve
8. . . .

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NLP concepts: Word Embeddings
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
A set of techniques which allow to map words (or phrases) to numeric vectors.
The words with similar meanings have “close” vectors.
word Vector
man [0.23, 0.56, …]
king [0.34, 0.16, …]
woman [0.41, 0.73, …]
queen [0.09, 0.62, …]
[king] – [man] + [woman] ≈ [queen]
Popular embeddings algorithms:
 Word2Vec
 fastText
 Glove
 . . .

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NLP concepts: Language Model
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
allows to compute a probability of a word in a sequence.
Where used? (spoiler: almost everywhere!)
Please, give me a … [ pen: 0.002, example: 0.0001, hand:0.08, … ]
• Machine translation
• Error correction
• Speech recognition
• Text generation

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NLP Tools
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
1 Online services
Python libraries
.NET Libraries
2
3
Azure Cognitive Services, IBM Watson, Amazon AI Services
NLTK, spaCy, skikit-learn,
gensim, Pattern
ML.NET, Microsoft.Speech,
Microsoft.Recognizers, Catalyst

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.NET libs: ML.NET
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet
Pros:
• Native for .NET (Core)
• Backed my Microsoft
• Super performant (at least MS says that )
• Extended with TensorFlow & more
NLP features:
• Text normalization
• Tokenizing
• N-gram
• Word embeddings
• Stop words removal Cons:
• Poor NLP features
• English-only (mostly)
• Not convenient for using separately from ML pipeline

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.NET libs: Catalyst
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
NLP features:
• Text normalization
• Tokenizing
• POS-tagging
• Word embeddings
• Stop words removal
https://github.com/curiosity-ai/catalyst
Pros:
• Native for .NET (Core)
• Inspired by spaCy library
• Fast tokenizer
• Has pretrained models
• Allows to train your own models
(based on Universal Dependencies project)
Cons:
• Early beta (or even alpha). Version 0.0.2795
• English-only (mostly)

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.NET libs: Microsoft.Recognizers
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
• Rule-based
• Recognizes numbers, units, date/time, etc
• Supports about 10 different languages
• Not only .NET (JavaScript, Python, Java)
• No support for Russian or Ukrainian 
https://github.com/Microsoft/Recognizers-Text/

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Other useful libraries
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
DEMO 1
Text summarization (extraction based) using home-brewed NLP
TEXT
Detect
language
Break into
sentences
Tokenize
and
get stems
sentence1 sentence2 sentence3
stem1 1 3 5
stem2 0 2 4
stem3 3 4 0
stem4 2 0 2
Bag of words
S1 S2 S3
S1 0 1.21 0.2
S2 1.21 0 3.56
S3 0.2 3.56 0
Similarity matrix
Page rank
algorithm
Summary
(top-rated
sentences)

25. Other useful libraries

26. Other useful libraries

27. Other useful libraries

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Other useful libraries
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
DEMO 2
Text summarization using ML.NET

29. Other useful libraries

30. Other useful libraries

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Other useful libraries
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
DEMO 3
Document tagging
(with TF-IDF and Catalyst POS tagging)

32. Other useful libraries

33. Other useful libraries

34. Other useful libraries

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Useful resources
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Universal Dependencies
https://universaldependencies.org/
Lang-uk
http://lang.org.ua/uk/
https://github.com/korzh/Korzh.NLP
All source code of this talk
Math.net – numerical computation algorithms for .NET
https://www.mathdotnet.com/
http://tiny.cc/dotnet-nlp-libs
List of .NET libraries with some NLP features

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Conclusions
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Catalyst library
looks promising but still a way to go
Contribute!
We can do NLP on .NET
(for the basic tasks at least)
ML.NET library
good and reliable but limited NLP features

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Other useful libraries
.NET CONFERENCE #1 IN UKRAINE KYIV 2019
Thank you!
Sergiy Korzh
Twitter: @korzhs
LinkedIn: https://www.linkedin.com/in/korzh/
Facebook: https://www.facebook.com/sergiy.korzh
Email: sergiy@korzh.com