1. From natural language to Bayesian Networks (and back)
Floris Bex
Utrecht University

2. Introduction
Analysts and decision-makers work with natural language text (or semi-structured arguments, scenarios)
Particularly in domains where numerical judgments are less common
Witness testimonies, expert reports, complaints, social media
Outlines, reports, timelines, mind-maps
Court arguments, rulings, reports

3. Introduction
Formal and structured methods can be powerful tools for decision support
Critical thinking & manual analysis
Dialectical analysis
Sensitivity analyses
(Semi-)automation of decision making
Formal ontologies
Argumentation and non-monotonic logics
Bayesian networks

4. Introduction Decision support systems for reasoning with evidence
Principled ways of going from text to formal models
Linguistic aspects
How do people express and interpret scenarios, probabilities, arguments?
Formal aspects
What are the relations between formalisms?
Design aspects
What are the goals of the system?
Psychological aspects
How do/should people reason?

5. From text to formalisms
Formal (math) models
Semi-structured or
unstructured text
Structured arguments
and scenarios

6. From text to formalisms
argumentation
Wigmore graphs
Ontologies
Bayesian
networks
Timelines
Reports, transcripts, etc
Formal (math) models
Semi-structured or
unstructured text
Structured arguments
and scenarios

7. Criminal complaint handling for police
Online criminal complaints (politie.nl)
How can we get the most/best information from the complainant?
How do we build a case based on multiple complaints?
Current intake is with a static form
Fields for names, addresses, bank accounts
Free text field “What happened?”
Examples of typically missing information:
How did the counterparty convince the victim to do business?
What other evidence of fraud is there?

8. Criminal complaint handling for police
Get structured information (scenario, argument) from form & text
Apply formal semantics for scenarios & arguments
What are the gaps in the story?
Which evidence is missing?
Ask for missing information
Give feedback about scenarios & arguments

9. Criminal complaint handling for police
Structure and formalize
information
Argument mining
scenario mining
Provide formal
semantics
Formal (math) models
Semi-structured or
unstructured text
Structured arguments
and scenarios
Text generation
Natural dialogues
Include or ask for new
information
Feedback of findings
to user

10. Demo

11. Understanding stories
I bought a Samsung S3 from Wesley. I paid him 45 euros

12. Understanding stories
Syntactic parsing
I bought a Samsung S3 from Wesley. I paid him 45 euros S v Od PP S v Od Oi

13. Understanding stories
Syntactic parsing
Named Entity Recognition
Floris
Samsung S3
Wesley
45 euros
I bought a Samsung S3 from Wesley. I paid him 45 euros S v Od PP S v Od Oi

14. Understanding stories
Domain ontology
sent to
sent by
Complainant
Product
Counterparty
paid to
Payment
paid by
Floris
Samsung S3
Wesley
45 euros
I bought a Samsung S3 from Wesley. I paid him 45 euros S v Od PP S v Od Oi

15. Understanding stories
Connect domain ontology to story (machine learning)
sent to
sent by
Complainant
Product
Counterparty
paid to
Payment
paid by
Floris
Samsung S3
Wesley
45 euros
I bought a Samsung S3 from Wesley. I paid him 45 euros S v Od PP S v Od Oi

16. Scenario reasoning Scenario from text
Floris bought a Samsung S3 from Wesley
Floris paid Wesley 45 euros

17. Scenario reasoning ? Is the scenario complete (ontology)?
The complainant bought product from the counterparty
The complainant made a payment to the counterparty
The counterparty sent the product to the complainant
Floris bought a Samsung S3 from Wesley
Floris paid Wesley 45 euros ?

18. Argumentative reasoning
Is there evidence for the scenario?
Floris bought a Samsung S3 from Wesley
Floris paid Wesley 45 euros ? ?

19. Argumentative reasoning
Is there evidence for the scenario?
Floris bought a Samsung S3 from Wesley
Floris paid Wesley 45 euros
s, Whatsapp
Bank statements

20. Criminal complaint handling for police
Ontological reasoning, reasoning with arguments & scenario’s
Complaint form(s)
Structured scenarios
Formal models
Semi-structured or
unstructured text
Structured arguments
and scenarios
Questions to
complainant
Structured
argumentation
dialogue

21. From arguments to Bayesian networks
Translate structured arguments based on evidence to Bayesian Networks
A formal account of constraints on a BN imposed by structured arguments
Semi-structured or
unstructured text
Structured arguments
and scenarios
Bayesian
networks

22. Structured argumentation: ASPIC+
Arguments are Directed Acyclic graphs
Nodes are statements in a logical language with neg.
Links are applications of inference rules (strict or defeasible)
Arguments constructed from knowledge base
Ke (evidence, certain premises), Kp (assumptions, uncertain premises)
Attack
On uncertain premises, on defeasible inferences, on conclusions

23. Structured arguments
The burglary (Bur) was committed by the suspect, because there is a footprint match (Ftpr) and a motive (Mot) backed by a report (For) and a testimony (Tes1), and the suspect has no alibi, so Opp.
Bur
Ftpr
Mot
Opp
For
Tes1

24. Structured arguments
However, there is evidence of a mixup in the lab (Mix), which means the footprint match is not really backed by evidence. Furthermore, the suspect later gave a testimony (Tes2) with an alibi, so −Opp.
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2

25. Bayesian Networks
Represent joint probability distribution as DAG + CPT
Directed Acyclic Graph
Nodes are variables Bur = [Bur, −Bur]
Arcs represent probabilistic dependencies between nodes (Mot, Bur)
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

26. Bayesian Networks
Observations E
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

27. Bayesian Networks Chains can be blocked, or inactive, given E if
s contains node with two incoming arcs which is not in E and has no descendants in E; or
s contains node in E that has at most one incoming arc on the chain.
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

28. Bayesian Networks Active chains are not blocked Tes1 Bur For Rel Mot
Ftpr
Mix

29. Bayesian Networks
Sets of variables X and Y are independent given E iff there is no active chain from X to Y
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

30. From arguments to constraints on BN
Nodes
Every proposition v or −v in the argument is a node representing variable v in the BN
Every proposition v in Ke is the observed value of v
Ftpr
Ftpr
For
For

31. From arguments to constraints on BN
Inference chains
For every rule v1,…, vn => vc / v1,…, vn −> vc used in an argument there is an active chain between nodes v1,…, vn and vc
Ftpr
Ftpr
Ftpr
Ftpr
v1…vn
For
For
For
For

32. From arguments to constraints on BN
Attack chains
For contradictory (i.e. Opp and −Opp) propositions, this is captured by inference chains
Opp
Opp
−Opp
v1…vn
Tes2
Tes2

33. From arguments to constraints on BN
Attack chains
If vu undercuts the application of rule vp => vc, then there are active chains from vp, vc to vu
Mix
Ftpr
For
Ftpr
Ftpr
Mix
For
Mix
For

34. From arguments to constraints on BN
Attack chains
If vu undercuts the application of rule vp => vc, then there are active chains from vp, vc to vu
Mix
Ftpr
Ftpr
Mix
vi…vj
vk…vn
For
For

35. Using arguments to check BNs
Differences in modelling between judge & BN expert
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

36. Using arguments to check BNs
Missing variables
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For ??
Rel
Mot
Ftpr
Mix

37. Using arguments to check BNs
Active inference chains
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

38. Using arguments to check BNs
Active inference chains
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

39. Using arguments to check BNs
Active attack chains
If Mix undercuts the application of rule For => Ftpr, then there are active chains from For, Ftpr to Mix
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

40. Using arguments to check BNs
Active attack chains
If Mix undercuts the application of rule For => Ftpr, then there are active chains from For, Ftpr to Mix
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
Tes1
Bur
For
Rel
Mot
Ftpr
Mix

41. Using arguments to build BNs
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
For
Tes1
Tes2
For
Mix
Ftpr
Mot
Opp
Mix
Bur

42. Using arguments to build BNs
Bur
Ftpr
Mot
Opp
−Opp
Mix
For
Tes1
Tes2
For
Tes1
Tes2
For
Mix
Ftpr
Mot
Opp
Mix
Bur

43. From arguments to constraints on BN
Probability constraints
Defeasible rule v1,…, vn => vc v1,…, vn is evidence for vc
Different interpretations of “is evidence for”
Pr(vc | v1,…, vn) > 0 (Verheij 2014)
Pr(vc | v1,…, vn) > 0.5 (Pollock 1995)
Pr(vc | v1,…, vn) > Pr(vc) (Hahn & Hornikx 2015) …

44. Constraints and multiple BNs
Constraints on structure and probabilities lead to multiple different BNs
Semi-structured or
unstructured text
Structured arguments
and scenarios
Bayesian
networks

45. Constraints and multiple BNs
Constraints on structure and probabilities lead to multiple different BNs
Compare BNs – where are the real differences?
Ask decision maker about these differences.
Questions to
decision maker
Structured arguments
and scenarios
Bayesian
networks

46. Conclusions
Principled and automated ways to go from text to formal models
Implement algorithms & translations into working systems!
Vlek et al.
Fenton, Neil, Lagnado
Timmer et al.
Formal (math) models
Semi-structured or
unstructured text
Structured arguments
and scenarios
Vlek et al.
Yet et al.
Timmer et al.