The document discusses the role of context in language processing and proposes a two-stage coherence optimization for improving machine understanding of language. It emphasizes the need for deep context comprehension through the integration of various knowledge sources and algorithms, while filtering incorrect options to optimize semantic coherence. The results indicate that employing a comprehensive semantic web approach can enhance the modeling of background knowledge in natural language processing tasks.

1. NED with two-stage
coherence optimization
Filip Ilievski, Marieke van Erp, Piek Vossen,
Wouter Beek & Stefan Schlobach
or
How I am teaching my bottle of Jack Daniel’s not to turn into a
168-years-old person with a net income of $120.000.000

2. Context
... is being persistently
avoided when processing
language by machines. No wonder. The context
is hard to quantify.
but the context lies in
the basis of the human
communication!

3. The burden of context in language
● The language is context-dependent
● Verbal context
○ Ford fell from a tree.
■ What is “Ford” ?
● Social context
○ What is “2+2” ?
■ In mathematics it is 4
■ In the car domain it is a car configuration: 2 front + 2
back seats
■ In psychology it is a family with 2 parents and 2 children

4. Lincoln increased the annual vehicle
sales to 300.000.
y was born in Lincoln.
Lincoln fell from a tree.
Lincoln was standing on the shelf. It
was covered in leather.
Shallow processing

5. Motivation
The shallow approaches can do only this much.
Claim #1: we need to deepen the processing.
Claim #2: context is a limitless inspiration
- verbal
- social
- domain
- spatial
- temporal
- discourse
- (you-name-it)

6. Shall we go a step further?

7. How to go about it
Combine many pieces (algorithms) in a puzzle (solution)
Use as extensive and global knowledge as possible:
Semantic Web
Natural Language
Processing
Lexical resources

8. Approach
Optimize the semantic coherence of the disambiguated
entities, while still excluding the verbally incorrect
options and skewing towards the domain and the
popularity of the entities.

9. Components
- Verb-based knowledge from NLP, VerbNet, FrameNet
and a domain ontology
- Domain skew (based on corpus analysis)
- Popularity of the candidates (from DBpedia)
- Semantic connectivity and similarity (based on DBpedia
information)
No module or knowledge source is perfect,
but >1 of both will be helpful !

10. System design

11. The background knowledge

12. Data
Annotated WikiNews articles
3 subcorpora:
- Airbus Boeing (30)
- General Motors (30)
- Stock Market (30)

13. Results

14. FrameNet+Domain ontology filter
Airbus GM Stock market
# links filtered 3 21 22
# incorrect
links filtered
3 13 19
# correct links
filtered
0 0 3
# not in GS
filtered
0 8 0
“Trading on Russia’s stock markets ...”
predicate: markets, Commerce_sell@Seller: Russia

15. Combinations

16. Conclusions
Context is useful
Semantic Web can help to
model background knowledge
We are still finding new
puzzle pieces

18. Thank You !

19. Appendices

20. Future
Get rid of the boring pipeline approach.
Use full-blown optimization system!

21. Resources
Grammatical structure
and meaning of words
Background knowledgeStructured linguistic
information
Semantic Web
Natural Language
Processing
Lexical resources

22. Example
“The United States transferred six detainees
from the Guantánamo Bay prison to Uruguay
this weekend, the Defense Department
announced early Sunday.”

23. State-of-the-art:
United States Guantanamo Bay Uruguay Defence Department
Geographical region GB detention camp Geographical region US Dept. of Defence
Fed. Government Place Football team Ministry of Defence of
Rep. of Korea
Men’s soccer team The naval base River
Women’s soccer team Battle of GB Rugby union team
Rugby union team U20 football team
Men’s ice hockey team U17 football team
Men’s basketball team
Secondary education in
US

25. VN: send-11.1
transferred
A0 is Animate or Organization
A0:United States
United States is Animate or
Organization
∏
A1: from
Guantanamo Bay
A2: to Uruguay
A1 is Location
A2 is Location
Guantanamo Bay is a location Uruguay is a location

26. VN: say-37.7
announced
A0 is Animate or OrganizationA0:the Defence
Department
∏
The Defence Department is an Animate or an Organization

27. After VerbNet
United States Guantanamo Bay Uruguay Defence Department
Geographical region GB detention camp Geographical region US Dept. of Defence
Fed. Government Place Football team Ministry of Defence of
Rep. of Korea
Men’s soccer team The naval base River
Women’s soccer team Battle of GB Rugby union team
Rugby union team U20 football team
Men’s ice hockey team U17 football team
Men’s basketball team
Secondary education in
US

29. Results

31. VN: send-11.1
transferred
A0 is Animate or Organization
A0:United States
United States is Animate or
Organization
∏
A1: from
Guantanamo Bay
A2: to Uruguay
A1 is Location
A2 is Location
Guantanamo Bay is a location Uruguay is a location

32. VN: say-37.7
announced
A0 is Animate or OrganizationA0:the Defence
Department
∏
The Defence Department is an Animate or an Organization

33. After VerbNet
United States Guantanamo Bay Uruguay Defence Department
Geographical region GB detention camp Geographical region US Dept. of Defence
Fed. Government Place Football team Ministry of Defence of
Rep. of Korea
Men’s soccer team The naval base River
Women’s soccer team Battle of GB Rugby union team
Rugby union team U20 football team
Men’s ice hockey team U17 football team
Men’s basketball team
Secondary education in
US