Francisco's Webber presentation, Inventor and Co-founder of cortical.io, who discusses how one can fundamentally understand how we can computationally model language and revolutionise semantic fingerprinting. Those are the slides from his presentations in Big Data Berlin, London, Paris, Munich and Vienna

1. © cortical.io inc. 2015
Empower your enterprise with
language intelligence
free access at
api.cortical.io
contact: f.webber@cortical.io

2. © cortical.io inc. 2015
who we are
• cortical.io inc. science startup in Vienna - Austria
• result of the CEPT project (Cortical Engine for Processing Text)
• advances in brain theory guided us to a fundamentally
new approach for natural language processing
• we are investor backed in the second round
• we made semantic fingerprinting accessible, robust,
scalable, intuitive and easy to use

3. © cortical.io inc. 2015
big (text) data
• businesses, organizations and
governments are threatened by the
big data explosion.
• a substantial part of this data
consists of text.
• computers ‘understand’ numbers
but ignore the meaning of
language

4. © cortical.io inc. 2015
the downsides
existing semantic systems are…
…hard to build (sometimes impossible)
…inaccurate & fragile (in real-world use)
…expensive to buy (licenses & services)
…tricky to integrate (setup, tuning, training)
…laborious to run (metadata management)
…hard to maintain (dictionaries, ontologies)

5. © cortical.io inc. 2015
Semantic Fingerprinting
5
• semantic fingerprinting bridges
the gap between natural
language processing and
knowledge management
• language is represented using
the same data format as found in
the neocortex (mammalian brain)
• the cortical.io Retina behaves like
a sensorial organ for language
• meaning is embodied in
thousands of self-learned
semantic features

6. © cortical.io inc. 2015
Semantic Fingerprinting
6
organ
piano
church liver
• the cortical.io Retina converts
every word into its semantic
fingerprint
• the fingerprints allow direct
semantic comparison of the
meanings between words
• similar fingerprints have similar
meanings

7. © cortical.io inc. 2015
Semantic Similarity
7
cat dogcat+dog
home & family
aspects
cat specific
aspects
dog specific
aspects
biology
aspects
38%

8. © cortical.io inc. 2015
word sense
disambiguation
rock
apple
computer
sense 1
sense 2
sense …n
songwriter
vocals
spector
airplay
album
seeds
flowers
pollinators
pests
insects
trees
fruit
sense 2a
vegetables
berries
ingredients
sugar
diet
sense 2 …m
food
macintosh
microsoft
linux
software
hardware

9. © cortical.io inc. 2015
Meaning Based Computing
9
jaguar porsche tiger- =

10. © cortical.io inc. 2015
Text Fingerprinting
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• word fingerprints can be
stacked together to form
fingerprints of any piece of text.
• all semantic fingerprint
properties remain: similar
fingerprints mean similar texts.
• representation is made through
more than 16K features.
aggregation+
sparsification
teens like to hear music on
their mobile phones
teens like to hear music on their mobile phones

11. © cortical.io inc. 2015
teens like playing good music
with their mobile phones
you can also consume chart
hits with your notebook27%
Text Similarity 1
11

12. © cortical.io inc. 2015
teens like playing good music
with their mobile phones
the fishermen are sailing out
of the harbor9%
Text Similarity 2
12

13. © cortical.io inc. 2015
similarity engineexample
document
most similar
documents
ordered along
the users
information need
query document index
result set
ranking
NLP Functionality:
Search

14. © cortical.io inc. 2015
NLP Functionality:
classification
cow elephantdog spider frog
“mammal
or
mammals
or
mammalian”
most relevant
matching area
Literally:

15. © cortical.io inc. 2015
Demos @ cortical.io
Demonstrations

16. © cortical.io inc. 2015
Evaluation
16
There are very few comparable algorithms: a couple
of academic ones that cannot be readily used for
production purposes and Google’s Word2Vec.
The MEN Test Collection: http://clic.cimec.unitn.it/~elia.bruni/MEN.html
The RG-65 Test Collection: http://www.aclweb.org/aclwiki/index.php?title=RG-65_Test_Collection_(State_of_the_art)
The WordSimilarity-353 Test Collection: http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/
Yu&Dredzde 2014: http://arxiv.org/pdf/1411.4166.pdf
Distributed representations of words and phrases: http://papers.nips.cc/paper/5021-di

17. © cortical.io inc. 2015
disciplines
of
language intelligence
• locate documents
• find web content
• match people
• identify products
• monitor competitors
• file business information
• discover new knowledge
• track customer satisfaction
• avoid duplication of work
• advertise on the Internet
• mine for evidences
• improve security

18. © cortical.io inc. 2015
business applications
“Anything that can be expressed with text can be matched:
- products with LinkedIn profiles,
- tweets with Facebook timelines,
- job descriptions with CVs …”

19. © cortical.io inc. 2015
into a stream of semantic
fingerprints
not
matching
convert the
twitter firehose
to generate a realtime
content sub-stream
MATCHMATCHMATCH
Filter
application:
streaming text filter

20. © cortical.io inc. 2015
resulting
filter
fingerprint
creating filter fingerprints
words
text
simple words, keywords
text or text-documents of any
size
profile descriptions or message
postings from social media
the expression builder allows
interactive design of boolean
specifications like:
jaguar - Porsche = tiger
the fingerprint editor allows the
“drawing” of fingerprints. The
meaning of the resulting
fingerprint can be monitored
through the context terms

21. © cortical.io inc. 2015
• match people by their profiles
• no keyword or field based
string matching limitations
• semantic similarity measure to
compare professional profiles
• different profiles for
professional, leisure, interests,
sports etc…
profile
fingerprint
activity
fingerprint
application:
profile matching

22. © cortical.io inc. 2015
• create fingerprints from product
descriptions
• find similar products by
matching description
fingerprints
• create customer fingerprints
from purchased products
product description
fingerprint
Product recommendations
similar products
recommendationsmatch
application:
product recommendation

23. © cortical.io inc. 2015
simplicity
• no prior expertise in natural language processing or linguistics
are needed.
• easy and intuitive definition of semantic filters or classifiers.
• all types of text (words, sentences, paragraphs, chapters, books,
etc…) are processed in the same way using fingerprints.
• easy expansion to other languages by switching to any of the
available language retinas.
• zero configuration and no parameter tweaking needed
cortical.io advantages

24. © cortical.io inc. 2015
cortical.io advantages
efficiency
• semantic fingerprints are small 2K byte sized binary vectors.
• only binary operators are used - no floating point operations
needed.
• linear scalability as the engine takes advantage of a parallel
computing infrastructure (multicore, cluster, virtualization) to
match any performance needed.
• high throughput as complex NLP operations are executed in a
single step and are therefore much faster than with traditional
statistical systems.

25. © cortical.io inc. 2015
quality
• higher precision on NLP operations due to the large number of semantic
features used (>16K).
• automatic disambiguation of human language due to the novel
approach.
• full language independence, equally high quality results in all languages
due to complete avoidance of any statistical language models.
• no unintended bias as no human input is needed as gold standard.
• automatic update as new words and concepts can be added
continuously.
cortical.io advantages

26. © cortical.io inc. 2015
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Contact : f.webber@cortical.io