Empower your Enterprise with language intelligence_Francisco Webber

© cortical.io inc. 2015

Empower your enterprise with language intelligence

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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


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


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)


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


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


Semantic Similarity

7cat dogcat+dog

home & family aspects

cat specificaspects

dog specificaspects

biologyaspects

38%


word sense disambiguation

rock

apple

computer

sense 1

sense 2

sense …nsongwriter

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


Meaning Based Computing

9

jaguar porsche tiger- =


Text Fingerprinting

10

• 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


teens like playing good music with their mobile phones

you can also consume chart hits with your notebook27%

Text Similarity 1

11


teens like playing good music with their mobile phones

the fishermen are sailing out of the harbor9%

Text Similarity 2

12


similarity engineexample document

most similar documents

ordered along the users

information need

query document index

result set

ranking

NLP Functionality: Search


NLP Functionality: classification

cow elephantdog spider frog

“mammal or mammals or mammalian”

most relevant matching area

Literally:


Demos @ cortical.io

Demonstrations

http://www.cortical.io/demos.html

http://www.cortical.io/demos.html


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

http://papers.nips.cc/paper/5021-di


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


business applications

“Anything that can be expressed with text can be matched: - products with LinkedIn profiles, - tweets with Facebook timelines, - job descriptions with CVs …”


into a stream of semantic fingerprints

not matching

convert thetwitter firehose

to generate a realtime

content sub-stream

MATCHMATCHMATCH

Filter

application: streaming text filter


resulting filter

fingerprint

creating filter fingerprintswords

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


• 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


• 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


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



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.


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.



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Data & Analytics

Empower your Enterprise with language intelligence_Francisco Webber