IBM Bluemix Paris Meetup #27 20171219 - Introduction to NLP with Recast.ai
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Transcript of IBM Bluemix Paris Meetup #27 20171219 - Introduction to NLP with Recast.ai
Introduction to NLP
Gil Katz [email protected] @gil_katz
Introduction to NLP
Gil Katz [email protected] @gil_katz
Collaborative end-to-end bot platform
BUILD CONNECT MONITOR TRAIN
1950: Turing Test (“Computing Machinery and Intelligence” / Alan Turing)
Historic Background
50’s-60’s: Machine translation to be reality in three to five years (Georgetown experiment)
60’s-80’s: NLP systems based on hard rules, some quite impressive!
80’s-90’s: Machine learning for NLP, starting with simple algorithms (decision trees)
Historic Background
90’s-2000’s: Increasingly statistical models are used (starting with HMM for POS tagging)
2000’s-today: Neural Networks added to the mix
NLU over translation Written over spoken Chatbots and Recast.AI
This Talk
State of the Art Intents Entities Skills
Intents Subject of communication
Entities Important objects
Skills Take action
greetings
I have no internet 4G doesn’t work No signal at my place
report-issue weather
report-issue greetings
I have no internet 4G doesn’t work No signal at my place
weather
report-issue greetings
I have no internet 4G doesn’t work No signal at my place
weather
report-issue greetings
I have no internet 4G doesn’t work No signal at my place
weather
- Where are you located?
Powered by Machine Learning
Code
ML
data answers
rules
answers
data rules data answers
Supervised Unsupervised
Classification Regression
Preprocessing Be Prepared!
J’ai envie d’une pizza aujourd’hui
J’ai envie d’une pizza aujourd’hui (fr)
J ’ ai envie d ’ une pizza aujourd’hui (fr) PRON
VERB
NOUN
ADP
DET
ADV
J ’ ai envie d ’ une pizza aujourd’hui (fr)
Intent Classification
Naive Bayes
Bayes’ Rule: P(i|w) = P(w|i)P(i)/P(w)
‘Sparse’ approach
L’enfant a mangé un poisson
Un poisson a mangé l’enfant
No context management
Neural Networks
Words are represented densely in an n-dimensional space
Representations are learned through context
Main problem: How to represent sentences?
vct(Berlin) - vct(Germany) + vct(France) = vct(Paris)
SVM
RNNs (LSTM/GRU/QRNN/...)
Bi-LSTM with Attention
What’s more?
Siamese Networks
Named Entity Recognition
Named Entity Recognition Sequence Labeling
I have no in yesterday since internet Nancy
I have no in yesterday since internet Nancy
O O O CONNECTION O LOCATION O DATETIME
? ? ? ? ? ? ? ?
I have no in yesterday since internet Nancy
? ? ? ? ? ? ? ?
I have no in yesterday since internet Nancy
PERSON
LOCATION
? ? ? ? ? ? ? ?
I have no in yesterday since internet Nancy
LOCATION
Hidden Markov Chains
SSVM
CRF
RNN (LSTM/GRU/QRNN/...)
What’s more?
Entity Enrichment
I have no internet in Nancy since yesterday
{
canonical: …
value: …
}
{
formatted: …
lat: …
lng: …
}
{
formatted: …
iso: …
accuracy: …
}
Dialog Management
Markov Decision Process Finite State Architecture Frame Based
User Initiative Greetings
System Initiative Payment
Mixed Initiative QA/Track/Order
Wrap Up
ML and NLP as engine behind chatbots
A chatbot is built on many ML tasks
Different algorithms fit different tasks
Recast.AI as an end-to-end chatbot development platform
Questions?