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Towards Semantics Generation

Third stage presentation of M.S project

Ashish Almeida

03M05601

Guide

Prof. Pushpak Bhattacharyya

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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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Roadmap

• Problem

• UNL*

• Linguistic analysis

• Attachment solution

• Dictionary creation

• Implementation

• Conclusion

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Current work (third stage)

• Organization of attributes

• Analysis of to-infinitive

• PRO-handling and resolution

• Acquisition of attributes for dictionary

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Problem • Semantics generation for sentences involving

lexeme to • Three problems

– Identifying the proper part of speech (POS)– Attachment ambiguity resolution– Handling PRO

• FocusOnly [V-N-to-N/V] frames considered.Document specific dictionary used

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UNL*

• UNL• UWs• Relations

John(icl>person)

give(icl>do)

Mary(iof>person)

flower(icl>flora)

agt golobj

@entry.@past

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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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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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Attachment algorithmFor Prepositional phrases

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Example • John gave a flower to Mary.

– Verb gave expects to

– Noun flower does not expect to

– Apply case 3

– Attach ‘to Mary’ to gave • Final UNL:

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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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UNL representationTheyi promised Mary [PROi to give a party].

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Attachment algorithm tablefor to-infinitival clauses

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PRO resolution

Example

a. He ordered us [to finish the work].

b. He ordered usi [PROi to finish the work].

Steps1. fetch PRO type fom dictionary entry of order2. Resolve all relations within clause

- [PROi to finish the work]

3. Relate the clause to verb order4. Finally replace the PRO with actual UW

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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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System

Resolve pro

Coindex the PRO

UNL expressions

Sentence having to

To-preposition

To-infinitive

Decide type and existence of PRO

Find semantic relation

Find attachment site

Find attachment site

Detect part of speech

Find semantic relation

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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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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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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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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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from Penn Treebank• Syntactically annotated corpus • Example

• Algorithm to extract this property

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Organizing attributes

• WordNet noun ontology explored.• The top level labels used as attributes.

• Example:

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English to UNL system

• Rule base

Inputsentence

UNLexpression

Partial UNLexpression

WordNet OALD Penn tree-bank

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Implementation

• POS Identification

• Finding Attachment site

• Creating Relation

• PRO insertion

• Post processing– Resolve the co-reference.

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Identification of POSPattern 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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Attachment rules

• Do noun attachment – 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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Handling PRO

1. 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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Replace PROExample

They promised Maryi [PROi to give a party].

agt (promise(icl>do).@entry.@past, they:0A)gol (promise(icl>do).@entry.@past, 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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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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ResultsTo Prepositio

n senseInfinitiv

esense

Total number of sentences(200) 100 100

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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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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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) http://www.unlc.undl.org

• Resources– OALD– WordNet– Penn Tree bank– DDG– Concordance search on Brown corpus– Beth Levin’s verb classes

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! Thank you