1. Syntactic Disambiguation through Lexicon Enrichment
Second Stage Project Presentation
Guide: Pushpak Bhattacharyya
Ashish Almeida
03M05601

2. Overview Motivation Problem definition Linguistic theory
Lexical enrichment
Design and implementation
Results
Future work
December 5, 2018

3. Motivation Robust and scalable UNL generation required
English analysis for extracting meaning
Correct analysis  correct meaning representation
Identification of correct syntactic representation
Identification of correct semantic relation
December 5, 2018

4. Preposition Phrase Attachment Problem
John covered the baby with a blanket.
covered
covered
John
the baby
with
Wrong !
John
the baby
a blanket
with
a blanket
Verb attachment
Noun attachment
December 5, 2018

5. Universal Networking Language
He forwarded the mail to the minister.
obj
agt
@ past
minister(icl>person)
forward(icl>send)
mail(icl>collection)
He(icl>person)
@def
gol
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6. Linguistic Insights Syntactic level Semantic level Syntactic Frame
Subcategorization
Semantic level
Selectional restrictions
Thematic/theta roles
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7. Syntactic Frame Sequence of words as they appear in sentences
[V-ART-N] … handed a book
[NP-to-NP] the mail to the minister
[V-NP-P-NP] … forwarded the mail to the minister
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8. Subcategorization Verbs He put the book on the table.
put: [ _NP PP-on]
Nouns
his reliance on/*at/*with her help.
*his reliance.
reliance: [ _PP-on]
Adjectives
He is fond of reading.
fond: [ _ PP-of]
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9. Selectional Restrictions
The boy murdered John.
*The boy murdered the tree.
Thus the verb ‘murder’ needs a human as object.
murder: [HUMAN _ HUMAN]
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10. Thematic Roles
Each argument of verb has a unique role associated with it.
Each role is assigned to a single argument.
E.g.
The boy murdered John.
The boy - agent
John - patient/theme
Other thematic roles : Instrument, locative, goal
UNL relations: analogous to thematic roles.
December 5, 2018

11. Lexicon Enrichment Idiosyncratic information
Subcategorization
Thematic roles in terms of UNL relations
How to get this information ?
Oxford advanced learner’s dictionary, WordNet
UNL relations
Beth Levin, manual effort
December 5, 2018

12. An Example Dictionary Entry
forward
E.g. he forwarded the mail to the minister
[forward]{}“forward(icl>sent)”
(VRB,VOA,VOA-PHSL, #_TO_A2,#_TO_A2_gol)<E,0,0>;
headword
Universal Word
Attributes
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13. Issues The work focuses on The [V-NP-P-NP] frame
Commonly used prepositions
In, on, of, with, from, to, for
Disambiguating to
Active voice
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14. Design Fill the valency of the nearest element first.
If in the frame [V-N1-P-N2]
both V and N1 have #P in their subcategorization frames, then satisfy the demand of the nearest element to P, i.e., the noun first.
Else, give priority to that element which subcategorizes the preposition P
Else, give priority to the events and actions (can be verb or noun)
destroyV, destructionN etc.
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15. Summarization of Algorithm
Conditions
Action
Attributes of V
NP1
NP2
Attachment of
Examples 1
#<P> _ N1
…paid a visit to the museum. 2
Not
...passed the ball to Bill.
…imposed heavy penalties on fuel dealers. 3 a
PLACE
…met him in his office.
TIME
…met him in the afternoon. b
#<P>,
EVENT
…cancelled a meeting with his friends. 5
…supplied plans for projects.
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16. Problems with to Infinitival to Preposition to Problem:
Do not allow onion to brown
Preposition to
The lights changed from green to brown
Problem:
Detect if the lexical element is to-preposition or to-infinitive
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17. Heuristics to Detect to-preposition
Criterion
Preposition to
Infinitival to
- to is followed by a determiner 
- to is followed by a word followed by a plural marker
- to is followed by an adjective
- to is followed by a proper noun
- to is followed by a pronoun
- the matrix verb specifies that it needs a to-
preposition complement.
- to is preceded by a noun which specifies that it
needs a to-preposition complement.
infinitival complement.
needs a to-infinitival complement.
- to is followed by a base verb
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18. Implementation Creating new dictionary with extra attributes
Writing new rules to use these new attributes
Rules to use subcategorization information
Rules for processing events (nouns and verbs)
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19. Analysis Engine (Enconverter)
sentence
Word1
Word2
Word3
Word4 …
Wordn
windows
LCW
LAW
RAW
RCW
Analysis windows
Left Analysis Window (LAW)
Right Analysis Window (RAW)
Condition windows
Many in number
LCWs, RCWs
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20. Operations in Analysis
Movement of heads
Addition of two nodes
Deletion of a node
Creating relation between two nodes
Adding dynamically inferred attributes to node
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21. Rules R{VRB,#_FOR_AR2:::}{N,#_FOR:::}(PRE,#FOR)P60; IF
; Right shift to affect noun attachment
R{VRB,#_FOR_AR2:::}{N,#_FOR:::}(PRE,#FOR)P60;
This states that IF
The left analysis window is on a verb which takes a for-pp as the second argument (indicated by #_FOR_AR2)
AND
The right analysis window is on a noun which takes a for-pp as an argument (indicated by #_FOR)
The preposition for follows the noun (indicated by (PRE,#FOR) )
THEN
Shift right (indicated by R at the start of the rule) anticipating noun attachment for the PP.
December 5, 2018

22. Other Rules <{VRB,#_FOR_AR2,#_FOR_AR2_rsn:::}
; Create relation between V and N2, after resolving the preposition preceding N2
<{VRB,#_FOR_AR2,#_FOR_AR2_rsn:::}
{N,FORRES,PRERES::rsn:}P25;
;Delete the preposition ON
>(VRB,EVENT,VOA){PRE,#ON:::}
{N,UNIT,TIME,DAY:+ONRES,+PRERES::}P27;
;Create the relation tim between verb and noun
<{VRB,VOA:::}
{N,TIME,UNIT,ONRES,PRERES::tim:}P20;
December 5, 2018

23. Testing Resources: Filtered out
British National Corpus
WordNet
Brown corpus
Filtered out
Phrasal verbs
Compound nouns
Longer sentences
Semantically different types of constructs
tested in [V-N-P-N] frame.
December 5, 2018

24. Cases of with
Different semantic Roles in different syntactic and semantic environments
Attachment
Semantic
relation
Example
Noun
obj
He cancelled [a meeting with his students].
and
She wore [a green skirt with a blouse].
Verb
ins
He [covered the baby with a blanket].
gol
That [provides him with a living].
ptn
He [is playing chess with his friend].
December 5, 2018

25. Results for of-preposition
The results of testing for solving PP attachment and generating UNL
Corpus
Frames
Total no. of Sentences
No. of Correct attachments & UNL relations
Accuracy %
BNC
V-N1-of-N2
1000
886 88
WSJ
661
597 90
December 5, 2018

26. Conclusion
Lexical enrichment originating from key linguistic principles makes the analysis more correct
Rule-base design simplified due to distinction made between complements and adjuncts during analysis
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27. Future Work Handling the alternation patterns of verbs
Applying the algorithm on all prepositions
Extracting the information through various resources
such as dictionaries and annotated corpus
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28. References
UNDL Foundation: The Universal Networking Language (UNL) specifications version 3.2. (2003)
Grimshaw, Jane.: Argument Structure. The MIT Press, Cambridge, Mass. (1990)
Brill, E. and Resnik, R.: A Rule based approach to Prepositional Phrase Attachment disambiguation. Proc. of the fifteenth International conference on computational linguistics. Kyoto. (1994)
Levin, Beth.: English verb Classes and Alternation. The University of Chicago Press, Chicago. (1993)
Hornby, A. S.: Oxford Advanced Learner’s Dictionary of Current English. Oxford University Press, Oxford.(2000)
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29. Thank you
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30. Example UNL In I deposited my money in my bank account. {unl} {/unl}
account(icl>statement):0W)
money(icl>currency):0F)
I:0C)
mod(money(icl>currency):0F, I:0C)
mod(account(icl> statement):0W, bank(icl>possession):0R)
mod(account(icl> statement):0W, I:0O)
{/unl}
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31. Example UNL On I put the book on the table. {unl} {/unl}
I:00)
{/unl}
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32. Example UNL To They served a wonderful meal to fifty delegates. {unl}
they(icl>thing):00)
wonderful(mod<thing):0E)
fifty(icl>number):0W)
{/unl}
December 5, 2018