1. NLP for business process automation practical cases
Deloitte Ukraine

2. Deloitte Ukraine Digital Solution Lab
Deloitte Global
150 countries
286,000 people wordwide
150 years of history
Ukrainian AI team
Data Scientists / NLP Specialists
Developers
UI/UX designers
Management team
Deloitte named a leader by Gartner in Data and Analytics Service Providers, Worldwide

3. Common NPL use cases in automation
Information extraction Semantic search Machine translation Data summarization Template fulfilling Dialogue support and navigation Documents classification and management

4. Deloitte use case 1: plenty web-sites analysis
Business need
Necessity to review, extract and summarize information from a large number of web-sites
Issue
Manual time-consuming process
Subjective decisions
Human-factor errors
Challenges
Multiple language content
Non-standard web-sites structure
Human-factor errors in data-labelling
Tasks
Text summarization
Text generation
Activity comparison

5. Text summarization and generation
Ideas:
Extract key words (wheat, sell) Combine them into the sentence (The company sells wheat)

6. Text summarization and generation
p(product)
p(activity)
p(none)

7. Text summarization and generation
LSTM
p(product)
p(activity)
p(none)

8. Text summarization and generation
p(product)
p(activity)
p(none)
Database of urls and summaries used Summary from DB: Development of robotic solutions
2/3 non-stop words found!

9. Text summarization and generation
Two approaches for text summarization
Extractive summarization
Abstractive summarization
Select parts (typically sentence) of the original text to form a summary
Easier
Too restrictive
Most past work is extractive
Generate novel sentences using natural language generation techniques.
More difficult
More flexible and human
Necessary for future progress

10. Text summarization and generation
Final predicted Vocab Distribution
1-Pgen
Pgen
Attention Distribution
Predicted Vocab Distribution
Attention
Encoder (BI-LSTM)
Input-Sequence
Decoder (RNN)
Context vector

11. Text summarization and generation
43%
of correct summaries
More experiments are on the way
Results:
Text summarization and generation
Joining two approaches:
Our 100% <activity> Made </activity> in Italy <product> robotic solutions </product> ensure cutting edge performance that will last over time.

12. Activity comparison
Idea:
Compare with GloVe or something better

13. Selection of the mimimum distance for each criteria type
Activity comparison
Input criteria
grain, wheat
storage
oil
manufacturing
machinery
wholesale, trade ?
Selection of the mimimum distance for each criteria type
0.51, 0.23
0.34
0.39
0.42
0.38
0.85, 0.31 ?
Each with each word comparison using GloVe algorithm*.
wheat (0.08,0.27)
crop (0.24, 0.1)
machinery (0.32, 0,6)
distance ~ 0.236
Text from web-site
We purchase, we accept for storage, we render services of completion and logistics of grain crops and sunflower.

14. Activity comparison ? Company 1: 0.31, 0.2, 0.45, 0.3
Random forest algorithm on 100 decision trees
Simple voting process
Probability of acceptance
…..
Company 7999: 0.51, 0.1, 0.1, 0.36,
X>0.3
Y<0.5
Z<0.8
Rejected
Accepted
W<0.8
True
False
Company 8000: 0.3, 0.22, 0.35, 0.67
0.31
0.23 ?
0.34
0.39
0.42
0.38

15. Probability of acceptance
92%
recall
50%
FPR
Results:
Activity comparison
Random forest algorithm
on 100 decision trees
Probability of acceptance

16. Deloitte use case 2: documents analysis
Business need
Necessity to store large number of documents in the database with certain attributes that enable search
Issue
Manual time-consuming process
Human-factor errors causing documents loose
Challenges
Multiple documents formats, including scanned pdfs
Tough deadlines
Poor labeling
Tasks
Document classification
Attribute extraction

17. Attributes extraction
Ideas:
Annotate texts Use LSTM

18. Attributes extraction
Receiving data from client
Conversion to wildcard expressions
tf-idf on limited vocabulary with logistic regression
Models blending
Key phrases:
The client hereby agrees to use…
Confidential work prepared for...
client * agrees to…
confidential work prepared * for…
Documents and general information

19. 95%
F 0.5
Results:
Attribute extraction

20. Possible solutions Advanced web-crawling Data capturing
Customized search and information scraping via internet for marketing researches, reputation analysis, threat intel, KYC etc.
Analysis, summarization and capturing of relevant information from web-sites, text documents, annual reports, 10K-reports etc.
Templates pre-populating
Data summarization & classification
Filling templates with text or quantitative data, selected or generated using cognitive algorithm.
Classification based on textual data in content of s, documents, agreements etc.

21. Q& A

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