1. The Complexity of OF in English
Rajat Kumar Mohanty
Ashish Francis Almeida
Srinivas Samala
Pushpak Bhattacharyya
Department of Computer Science and Engineering
Indian Institute of Technology Bombay
ICON 2004, IIIT, Hydrabad

2. Outline Motivation Distribution of OF-NP Constructions
Introduction to Universal Networking Language
Design and Implementation
Evaluation
Conclusion
Future Work
ICON 2004
2/25/2019

3. Motivation Prepositional Phrase Attachment Fundamental problems
Given the frame [V-NP1-of-NP2]
Whether NP2 attaches to V or NP1
if the attachment site is NP1,
then which is the semantic head: NP1 or NP2?
What is the semantic relation between V and N2, or N1 and N2?
ICON 2004
2/25/2019

4. Distribution of of-NP Constructs
Syntactic Environments
Types of of-construction
Semantic Heads at a glance
Argument Structure of Lexical Items
ICON 2004
2/25/2019

5. Syntactic Environments
[NP1-of-NP2-V]
The comedy of Shakespeare is worth reading.
[V-of-NP]
She always boasts of her skills.
[V-A-of-NP]
He is aware of the situation.
[V-NP1-of-NP2]
I remind the committee of the suggestion.
[V-NP1-of-NP2-of-NP3]
I informed the author of the book of this comment.
ICON 2004
2/25/2019

6. The Frame: [NP1-of-NP2] Associative construction
The comedy of Shakespeare
Partitive construction
One of these pens does not work.
Kind-construction
Three kinds of cake are available here.
Exercises of this kind are very popular.
ICON 2004
2/25/2019

7. Detecting Semantic Head
Sub-Category
of of-NP
construction
Semantic Head
Examples
Associative N1 A
[the value of silence]
[aware of the situation]
Partitive N2
[two tons of rice]
Kind-construction
N2 (Kind-initial)
N1 (Kind-final)
[an animal of that kind]
[that kind of animal]
of-adjunct V
He cleared the desk of papers.
of-argument
The Police informed the parents of the accident.
ICON 2004
2/25/2019

8. Argument Structure and Thematic Roles
The man put the book on the table.
put V
__ NP PP
Event PUT
([Thing THE MAN],
[Thing THE BOOK],
[Place ON THE TABLE])
ICON 2004
2/25/2019

9. Argument Structure and Thematic Roles
inform
e.g., We can still inform [the public] [of the particular models]. V
__ NP PP
donation
e.g., A donation [of $50,000] could have been made to the charity. N
__ PP
aware A
e.g., He was aware [of the situation].
__ PP
ICON 2004
2/25/2019

10. The World-wide Universal Networking Language (UNL) Project
Marathi
English
Russian
UNL
Japanese
Spanish
Hindi
Language independent meaning representation.
Our goal : Robust and scalable UNL generation for multilingual meaning-based search engines.
ICON 2004
2/25/2019

11. UNL Framework Analysis System (EnConverter) The Analyzer Machine
The Lexicon
The Rule Base
The Analyzer Machine
EnConvertor
Dictionary
Rule base
sentence
UNL
ICON 2004
2/25/2019

12. inform(icl>communicate) people(icl>group)
UNL Graph
We Informed the people of the models.
inform(icl>communicate)
@ past
gol
agt
obj we
people(icl>group)
@def
model(icl>concept)
ICON 2004
2/25/2019

13. UNL Expression {unl} agt(inform(communicate).@entry.@past, we)
We informed the people of the models.
{unl}
we)
{\unl}
ICON 2004
2/25/2019

14. The Lexicon [John] “John(iof>person)” (N,MALE,PROPER,ANIMATE)
John ate rice with a spoon.
[John] “John(iof>person)” (N,MALE,PROPER,ANIMATE)
[eat] “eat(icl>do)” (VRB,VoI)
[rice] “rice(icl>food)” (N,FOOD)
[spoon] “spoon(icl>artifact)” (N,INSTR)
Headword
Universal Word
Attributes
ICON 2004
2/25/2019

15. The Rule Base “If condition then action” type rules Types Priority
Move Left-Right
Create relations
Delete node
Add Attribute
Priority
ICON 2004
2/25/2019

16. The Analyzer Machine W2 W1 W4 … Wn W3
LAW: Left Analysis Window (processing head)
RAW: Right Analysis Window
LCW: Left Condition Window (context head)
RCW: Right Condition Window
W1,W2..Wn : words in the sentence
LAW W2 W1 W4 …
RAW Wn W3
RCW
LCW
ICON 2004
2/25/2019

17. Design and Implementation
Attachment Possibilities
Strategy
Lexical Conditions
Enrichment of Lexicon
UNL Rules for PP-Attachment
UNL Rules for Semantic Relation
ICON 2004
2/25/2019

18. Attachment Possibilities
The frame [V-NP1-P-NP2] V
NP1
NP2
(A)
…remind him of Gita
.. saw the book of physics
... drank a cup of milk
(B)
(C)
ICON 2004
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19. Strategy Case A She always boasts of her skills - P deletion V-of-N
- rel(V, N)
(no attachment problem)
ICON 2004
2/25/2019

20. Case B N1-of-N2 The comedy of Shakespeare is worth reading.
A bundle of rags
N1-of-N2
- N2 (head)
- rel(N2,N1)
(no attachment problem,but headedness)
- N1 (head)
- rel(N1,N2)
ICON 2004
2/25/2019

21. Case C V-N1-of-N2 Bill drained the sink of the water.
A good listener knows the value of silence.
- rel(V, N1)
- N1 deletion
- case of (A)
V-attachment
V-N1-of-N2
(attachment problem)
-case of (B)
N1-attachment
ICON 2004
2/25/2019

22. Case D V-A-of-N He is aware of the situation. - rel(V, A) - rel(A, N1)
(always attached to A)
ICON 2004
2/25/2019

23. Case E He informed the author of the book of the criticism.
- N1 is the Head.
- rel (N1 , N2 )
- V-N1-of2 -N3
- case of (C)
V- (N1-of1 -N2)-of2 -N3
- N2 is the Head.
- rel (N2 , N1 )
V-N2-of2 -N3
case of (C)
V- N1-of1 -N2-of2 -N3
(attachment problem)
- N1 is the Head.
- rel (N1 , N2 )
- V-N1-of2 -N3
- case of (C)
V- N1-of1 –(N2-of2 -N3)
- N2 is the Head.
- rel (N2 , N1 )
- V-N2-of2 -N3
- case of (C)
ICON 2004
2/25/2019

24. Decision Table for of-NP Attachment
Lexical Conditions
Action
Attributes of V
Attributes of NP1
Attachment of NP2
Examples 1
V (OF)
N (OF) N1
He reminded [the author of the book] [of the comment]. 2
N (^OF) V
He informed [the police] [of the danger]. 3
V (^OF)
Few people doubt the zeal of the movement 4
I met the author of the book yesterday.
ICON 2004
2/25/2019

25. Attachment Conditions
A. NP2 is attached to V, only when
(V has #_OF attribute) AND (N1 does not have it); see row 2,
Otherwise
B. NP2 is attached to N1 when
(both V and N1 have #<OF> attribute); see row 1 OR
(V does not have #<OF>) AND (N1 has it); see row 4
(Neither V nor N1 has #<OF>, in which case combinations of attributes of V, N1 or N2 determine the attachment site); see row 3
ICON 2004
2/25/2019

26. Enrichment of Lexicon [inform]{}“inform(icl>communicate)”
(VRB,VOA,VOACOMM,#_OF_AR2,#_OF_AR2_obj) <E,0,0>;
[donation]{}“donation(icl>act)” (N,ABS,ACT,#_OF,#_OF_obj) <E,0,0>;
[proud]{}“proud(aoj>thing)” (ADJ,DES,EMOT,#_OF,#_OF_obj) <E,0,0>;
[die]{}”die(icl>be)” (V,VOS,#_OF,#_OF_rsn)<E,0,0>;
ICON 2004
2/25/2019

27. UNL Relations mod(career(icl>line, officer(icl>policeman))
career of the officer
obj(know(icl>be,lead(icl>position))
Ministry knew of lead in milk
qua(paper(icl>material), bundle(icl>quantity)
bundle of paper
ICON 2004
2/25/2019

28. UNL Rule for PP-Attachment
;Right shift to affect Noun attachment
R{VRB,#_OF_AR2:::}{N,#_OF:::}(PRE,#OF)P60; IF
the left analysis window is on a verb which takes an of-NP as the second argument (indicated by #_OF_AR2)
AND
the right analysis window is on a noun which takes an of-NP as an argument (indicated by #_OF)
the preposition of follows the noun (indicated by (PRE,#OF))
THEN
Shift right (indicated by R at the start of the rule)
(anticipating noun attachment for the pp).
ICON 2004
2/25/2019

29. UNL Rule for a Semantic Relation
;Create relation between V and N2, after resolving the preposition preceding N2
<{VRB,#_OF_AR2,#_OF_AR2_obj:::}{N,OFRES,PRERES::obj:}P25; IF
the left analysis window is on a verb which takes a of-NP
as the second argument which should be linked with the
obj relation (indicated by #_OF_AR2_OBJ)
AND
the right analysis window is on a noun for which the preceding preposition has been processed and deleted
THEN
set up the obj relation between V and N2.
ICON 2004
2/25/2019

30. Evaluation Linguistic Analysis Creation of Testbed UNL Generation
Oxford Genie
Web Concordancer (
Wordnet 2.0
BNC
Creation of Testbed
Sentence Extraction (12-15 words)
Isolation of Phrasal Verb and Compound Nouns
WSJ
V-NP1-of-NP2
UNL Generation
ICON 2004
2/25/2019

31. Statistics Verb-Attachment Cases in BNC Partitive Cases in BNC
#Total [V-N1-of-N2] = 1000
#Verb attachment cases = 9
#Noun attachment cases = 991
Partitive Cases in BNC
# [NP-of-NP] = 1140
Proportion of partitive cases = 17.3 % (197)
Average accuracy for partitives = 92 % (182)
ICON 2004
2/25/2019

32. Statistics Accuracy of UNL Generation V-of-N 100 87 13 87 % N1-of-N2-V
Frames
Total no. of Sentences
No. of Correct attachments & UNL relations
No. of Incorrect cases
Accuracy %
V-of-N
100 87 13
87 %
N1-of-N2-V 92 8
92 %
V-N1-of-N2
1000
886
114
88 %
V-N1-of-N2(WSJ data)
661
597 64
90 %
V-A-of-N
300
237 63
79 %
V- N1 –of – N2 –of-N3 50 39 11
78 %
ICON 2004
2/25/2019

33. Conclusion A Detailed Study of the of-NP Constructs Exciting Result
Headedness
Attachment Properties
Semantic Relations
Exciting Result
Head-detection Accuracy: 92%
Overall Accuracy: 86 %
Outcome
Lexicon Enrichment with Conceptual Structure Information
ICON 2004
2/25/2019

34. Future Work
Extending the work to other frames associated with of (viz., [of-Ving], [of-CP])
Extending the same Algorithm to other Prepositions
Lexicon Enrichment
Argument Structure Information
Thematic Role Information
Selectional Preferences
Use of Annotated Corpus to ease the Process
ICON 2004
2/25/2019

35. Q&A
THANK YOU!
ICON 2004
2/25/2019