GPU Accelerated Natural Language Processing by Guillermo Molini

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This document discusses natural language processing (NLP) and how it can be used for tasks like searching, information extraction, and speech recognition. It explains how traditional searching works by matching keywords versus modern NLP techniques like vector embeddings that represent words as vectors in a multi-dimensional space. Vector embeddings allow determining semantic similarity between words and can be used for applications like speech recognition. The document also discusses how GPUs can accelerate NLP tasks by parallelizing computations and presents Wavecrafters' solution for providing GPU-accelerated NLP capabilities.

Technology

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GPU Accelerated Natural
Language Processing

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Roadmap
What’s NLP?
Traditional Search.
Modern NLP. Vector Embeddings.
Speech to Text
Demo

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Natural Language Processing
Computational techniques used for analysing and representing
text for the purpose of achieving human-like language
processing.

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Uses
• Searching
• Information Extraction
• Summarization
• Question Answering
• Customer Interaction
• Sentiment Analysis
• Speech to Text

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How does traditional searching work?
• Stemming
• Synonyms
• Tags
• Misspelling support
• Ranking

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An Example
A seal

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Traditional text search
Its just matching!

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How do Vector Embeddings work?
0.13 -0.01 0.56 0.32 0.39 -0.79 0.86 0.55 0.22 0.19
Seal
Vector of n dimensions

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How do Vector Embeddings work? (II)
Training

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How do Vector Embeddings work? (III)
Training
• Different training algorithms: GloVe (Socher, Standford University), Word2Vec
(Google), Doc2Vec (Mikolov, Facebook).
• We will be releasing shortly our own GPU based version of GloVe as open-source.

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How do Vector Embeddings work? (IV)
• Vectors cosines give us the semantic closeness.
Ball
Mars
Ball
Football
But we can also do much
more! Adding, subtracting…

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How do Vector Embeddings work? (VI)
United Kingdom London MadridSpain

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Why aren’t Vector Embeddings
widespread?
• Steep Learning curve. Math can be
complicated.
• Lots of computational power
needed. Slow and expensive.

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Our solution: GPU Computing

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Advantages of GPUs (II)
115ms
11632ms
0
2000
4000
6000
8000
10000
12000
14000
Semantic closeness to 10.000.000 documents. Lower is better!
GPU Execution Time CPU Execution Time

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Demo
Searching in a database of web scraped news.

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Speech to Text.
Ability to automatically transcribe video / audio into its
written form.

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Speech to Text. Uses
• Information Extraction
• Close Captioning
• Summarization
• Searching

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Speech To Text (I). Get the phonemes.
the
co
te
a
me
ma
lo

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Speech To Text (II). From Phonemes to
Words
A
Ball
Ballet
Bull
Market
Mars
Marsh
0.15
0.12
0.10
0.58
0.24
0.13
0.05
0.03
0.07
0.56

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Speech To Text (III). From Phonemes to
Words
the
co
te
a
me
ma
lo
Dictionary of probabilities
A ball market is a good chance
for investors

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Speech to Text (IV)
A ball market is a good chance for investors

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Speech to Text (IV)
A bull market is a good chance for investors

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Speech to Text (VI). Improving the Error
Rate
• Do several rounds of processing.
• In each one, use NLP to find out the theme of the
conversation, then produce a new Language Model (Dictionary)
that fits the theme.
• Reprocess the input
• Costly and slow!

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Q&A
Vicente Cuéllar, CEO
vicente.cuellar@wavecrafters.com
647523358
Guillermo Moliní, CTO
guillermo.molini@wavecrafters.com
610477054

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GPU Accelerated Natural Language Processing by Guillermo Molini

2. Powered by WAVECRAFTERS GPU Accelerated Natural Language Processing

3. Powered by WAVECRAFTERS Roadmap What’s NLP? Traditional Search. Modern NLP. Vector Embeddings. Speech to Text Demo

4. Powered by WAVECRAFTERS Natural Language Processing Computational techniques used for analysing and representing text for the purpose of achieving human-like language processing.

5. Powered by WAVECRAFTERS Uses • Searching • Information Extraction • Summarization • Question Answering • Customer Interaction • Sentiment Analysis • Speech to Text

6. Powered by WAVECRAFTERS Roadmap What’s NLP? Traditional Search. Modern NLP. Vector Embeddings. Speech to Text Demo

7. Powered by WAVECRAFTERS How does traditional searching work? • Stemming • Synonyms • Tags • Misspelling support • Ranking

8. Powered by WAVECRAFTERS An Example A seal

10.

11.

12.

13. Powered by WAVECRAFTERS Traditional text search Its just matching!

14. Powered by WAVECRAFTERS Roadmap What’s NLP? Traditional Search. Modern NLP. Vector Embeddings. Speech to Text Demo

15. Powered by WAVECRAFTERS How do Vector Embeddings work? 0.13 -0.01 0.56 0.32 0.39 -0.79 0.86 0.55 0.22 0.19 Seal Vector of n dimensions

16. Powered by WAVECRAFTERS How do Vector Embeddings work? (II) Training

17. Powered by WAVECRAFTERS How do Vector Embeddings work? (III) Training • Different training algorithms: GloVe (Socher, Standford University), Word2Vec (Google), Doc2Vec (Mikolov, Facebook). • We will be releasing shortly our own GPU based version of GloVe as open-source.

18. Powered by WAVECRAFTERS How do Vector Embeddings work? (IV) • Vectors cosines give us the semantic closeness. Ball Mars Ball Football But we can also do much more! Adding, subtracting…

19. Powered by WAVECRAFTERS How do Vector Embeddings work? (VI) United Kingdom London MadridSpain

20. Powered by WAVECRAFTERS Why aren’t Vector Embeddings widespread? • Steep Learning curve. Math can be complicated. • Lots of computational power needed. Slow and expensive.

21. Powered by WAVECRAFTERS Our solution: GPU Computing

22. Powered by WAVECRAFTERS

23. Powered by WAVECRAFTERS Advantages of GPUs (II) 115ms 11632ms 0 2000 4000 6000 8000 10000 12000 14000 Semantic closeness to 10.000.000 documents. Lower is better! GPU Execution Time CPU Execution Time

24. Powered by WAVECRAFTERS Roadmap What’s NLP? Traditional Search. Modern NLP. Vector Embeddings. Speech to Text Demo

25. Powered by WAVECRAFTERS Demo Searching in a database of web scraped news.

26.

27. Powered by WAVECRAFTERS Roadmap What’s NLP? Traditional Search. Modern NLP. Vector Embeddings. Speech to Text Demo

28. Powered by WAVECRAFTERS Speech to Text. Ability to automatically transcribe video / audio into its written form.

29. Powered by WAVECRAFTERS Speech to Text. Uses • Information Extraction • Close Captioning • Summarization • Searching

30. Powered by WAVECRAFTERS Speech To Text (I). Get the phonemes. the co te a me ma lo

31.

32. Powered by WAVECRAFTERS Speech To Text (II). From Phonemes to Words A Ball Ballet Bull Market Mars Marsh 0.15 0.12 0.10 0.58 0.24 0.13 0.05 0.03 0.07 0.56

33. Powered by WAVECRAFTERS Speech To Text (III). From Phonemes to Words the co te a me ma lo Dictionary of probabilities A ball market is a good chance for investors

34. Powered by WAVECRAFTERS Speech to Text (IV) A ball market is a good chance for investors

35. Powered by WAVECRAFTERS Speech to Text (IV) A bull market is a good chance for investors

36. Powered by WAVECRAFTERS Speech to Text (VI). Improving the Error Rate • Do several rounds of processing. • In each one, use NLP to find out the theme of the conversation, then produce a new Language Model (Dictionary) that fits the theme. • Reprocess the input • Costly and slow!

37.

38. Powered by WAVECRAFTERS Q&A Vicente Cuéllar, CEO vicente.cuellar@wavecrafters.com 647523358 Guillermo Moliní, CTO guillermo.molini@wavecrafters.com 610477054

GPU Accelerated Natural Language Processing by Guillermo Molini

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