gpu accelerated natural language processing by guillermo molini

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GPU Accelerated Natural Language Processing


RoadmapWhat’s NLP?

Traditional Search.

Modern NLP. Vector Embeddings.

Speech to Text

Demo


Natural Language Processing

Computational techniques used for analysing and representingtext for the purpose of achieving human-like languageprocessing.


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



Traditional Search.


Speech to Text

Demo


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


An Example

A seal


Traditional text search

Its just matching!



Traditional Search.


Speech to Text

Demo


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


How do Vector Embeddings work? (II) Training


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.


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…


How do Vector Embeddings work? (VI)

United Kingdom London MadridSpain


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


Our solution: GPU Computing


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



Traditional Search.


Speech to Text

Demo


Demo

Searching in a database of web scraped news.



Traditional Search.


Speech to Text

Demo


Speech to Text.

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


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


Speech To Text (I). Get the phonemes.

the

co

tea

mema

lo


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


Speech To Text (III). From Phonemes to Words

the

co

tea

mema

lo

Dictionary of probabilities

A ball market is a good chance for investors


Speech to Text (IV)

A ball market is a good chance for investors


Speech to Text (IV)

A bull market is a good chance for investors


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


Q&A

Vicente Cuéllar, [email protected]

Guillermo Moliní, [email protected]

mailto:[email protected]

mailto:[email protected]

gpu accelerated natural language processing by guillermo molini

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