1. Towards Semantics Generation
Third stage presentation of M.S project
Ashish Almeida
03M05601
Guide
Prof. Pushpak Bhattacharyya
2/17/2019

2. Motivation Goal: semantic role labeling
To commonly used functional element in English. (34% (source: Penn tree-bank))
To act as both preposition and as infinitival marker.
PRO was not considered before in semantic labeling
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3. Roadmap Problem UNL* Linguistic analysis Attachment solution
Dictionary creation
Implementation
Conclusion
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4. Current work (third stage)
Organization of attributes
Analysis of to-infinitive
PRO-handling and resolution
Acquisition of attributes for dictionary
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5. Problem Semantics generation for sentences involving lexeme to
Three problems
Identifying the proper part of speech (POS)
Attachment ambiguity resolution
Handling PRO
Focus
Only [V-N-to-N/V] frames considered.
Document specific dictionary used
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6. flower(icl>flora)
UNL*
give(icl>do)
gol
agt
obj
John(icl>person)
Mary(iof>person)
flower(icl>flora)
UNL
UWs
Relations
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7. Differentiating POS
Identify to-preposition phrase from to-infinitival clause
… gave papers to the judge
- to is followed by a determiner
… increases to 25 million rupees
- to is followed by a number
… to cooks.
- to is followed by a plural noun
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8. Differentiating POS … to-infinitival
…to go…
- to is followed by a base verb
… to clearly write…
- to is followed by adverb followed by base verb.
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9. Attachment algorithm
For Prepositional phrases
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10. Example John gave a flower to Mary. Final UNL: Verb gave expects to
Noun flower does not expect to
Apply case 3
Attach ‘to Mary’ to gave
Final UNL:
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11. To infinitival clauses
Example
1a. He promised me [to come for the party].
1b. Hei promised me [PROi to come for the party].
promise  subject controlled pro
2a. They forced Mary [to give a party].
2b. They forced Maryj [PROj to give a party].
force  object controlled pro
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12. UNL representation Theyi promised Mary [PROi to give a party].
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13. Attachment algorithm table
for to-infinitival clauses
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14. PRO resolution Example a. He ordered us [to finish the work].
b. He ordered usi [PROi to finish the work].
Steps
fetch PRO type fom dictionary entry of order
Resolve all relations within clause
- [PROi to finish the work]
Relate the clause to verb order
Finally replace the PRO with actual UW
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15. Semantic relations Filled using the Levin’s verb classes.
No semantically role resource available
Stored in dictionary along with argument information
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16. System Sentence having to Detect part of speech Find attachment site
To-infinitive
To-preposition
Find attachment site
Decide type and existence of PRO
Find attachment site
Resolve pro
Find semantic relation
Find semantic relation
Coindex the PRO
UNL expressions
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17. Dictionary All words must be present in dictionary Structure
[letter] “letter(icl>document)” (N,INANI,PHSCL) <E,0,0>
headword
Universal word
Attributes
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18. Dictionary: Acquisition of attributes
New attribute needed to apply the algorithm
Argument structure information
Semantic relations
PRO control property of verbs
Oxford, WordNet
Penn Treebank
Beth Levin’s verb classification
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19. from WordNet Sentence frames for verbs Example For verb want
They ____ him to write the letter.
For the verb promise
Somebody ----s somebody to INFINITIVE
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20. from Oxford dictionary
Oxford advanced learners dictionary (OALD)
provides partial frames wherever applicable
Examples
effort noun
…… 2 [C] ~ (to do sth) an attempt to do sth especially when it is difficult to do: to make a determined / real / special effort to finish on time …..
force verb
make sb do sth 1 [often passive] ~ sb (into sth / into doing sth) to make sb do sth that they do not want to do SYN COMPEL …• [VN to inf] I was forced to take a taxi because the last bus had left. • She forced herself to be polite to them. …
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21. from Penn Treebank Syntactically annotated corpus Example
Algorithm to extract this property
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22. Organizing attributes
WordNet noun ontology explored.
The top level labels used as attributes.
Example:
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23. English to UNL system Enconverter Post editor Rule base WordNet OALD
Partial UNL
expression
UNL
expression
Input
sentence
Enconverter
Post editor
WordNet
OALD
Penn tree-bank
Rule base
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24. Implementation POS Identification Finding Attachment site
Creating Relation
PRO insertion
Post processing
Resolve the co-reference.
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25. Identification of POS Pattern to detect to infinitive:
-to followed by verb in base form
:{:::}{^TO_INF_NEXT:+TO_INF_NEXT::}(#TO,TO_INF)(BLK)(VRB,V_1)P40;
IF (The left analysis window (indicated by {}) is on any word)
AND
(The right analysis window is on a word which does not have a TO_INF_NEXT i.e. look ahead is not performed yet. )
THEN
Select the next sequence of words such that they will satisfy the conditions as –
pick the word to corresponding to infinitival-to (indicated by attributes #TO and TO_INF)
AND pick a space (indicated as BLK)
AND pick a verb which is in its simple form (indicated by V_1)
AND add the property TO_INF_NEXT to the word in the right analysis window
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26. Attachment rules Do noun attachment Create goal relation
Move ahead when on frame [V][N]-P-N
R{VRB,#_TO_AR2:::}{N,#_TO:::}(PRE,#TO)P60;
Create goal relation
gol(uw1, uw2)
<{VRB,#_TO_AR2,#_TO_AR2_gol:::}{N,TORES,PRERES::gol:}P25;
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27. Handling PRO
Produce a “PRO” element in UNL with appropriate relation. (Enconverter)
:{VRB,SUB_PRO:::}"[[SUB_PRO]]:N,SUB_PRO,
#INSERTED::"(VRB,TO_INFRES,^PRORES)P30;
2. Relate it to the verb of the infinitive clause semantically. (Enconverter)
>(VRB){N,SUB_PRO::agt:}{VRB,VOA,TO_INFRES:
+PRORES,+SUB_PRORES::}P40;
3. Substitute a referred UW in the place of PRO. (Post editor)
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28. Replace PRO Example They promised Maryi [PROi to give a party].
agt they:0A)
gol Mary(iof>person))
obj (promise(icl>do), :01)
agt :01(give(icl>do), sub_PRO:0C)
obj :01(give(icl>do), party(icl>function))
After post processing
agt :01(give(icl>do), they:0A)
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29. Evaluation Preparation of test sentences
Source : Penn Treebank, edict concordencer and Oxford
Dictionary
Automatic dictionary generator
Editing and corrections
Appending extra attributes.
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30. Results To Preposition sense Infinitive sense
Total number of sentences(200)
100
Number of sentences where correct sense of to is detected 97 93
Number of sentences with correct attachment/UNL 80 72
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31. Conclusion Automatic acquisition of attributes is effective.
Correct Semantic representation is crucial.
Helps in applications like information retrival, generation to other language, question answering
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32. References
Grimshaw, Jane: Argument Structure. The MIT Press, Cambridge, Mass. (1990)
Mohanty R.K., Almeida A., Srinivas S., Bhattacharyaa P.: The complexity of OF, ICON, Hydrabad, India. (2004)
UNDL Foundation: The Universal Networking Language (UNL) specifications version 3.2. (2003)
Resources
OALD
WordNet
Penn Tree bank
DDG
Concordance search on Brown corpus
Beth Levin’s verb classes
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33. ! Thank you
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