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CS626-449: NLP, Speech and Web-Topics-in-AI

CS626-449: NLP, Speech and Web-Topics-in-AI. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 37: Semantic Role Extraction (obtaining Dependency Parse). Vaquious Triangle. Interlingua based (do deep semantic process Before entering the target language). Generation. Analysis.

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CS626-449: NLP, Speech and Web-Topics-in-AI

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  1. CS626-449: NLP, Speech and Web-Topics-in-AI Pushpak BhattacharyyaCSE Dept., IIT Bombay Lecture 37: Semantic Role Extraction (obtaining Dependency Parse)

  2. Vaquious Triangle Interlingua based (do deep semantic process Before entering the target language) Generation Analysis Transfer Based (do deep semantic process Before entering the target language) Vaquious: an eminent French Machine Translation Researcher- Originally a Physicist Direct (enter the target Language immediately Through a dictionary)

  3. Universal Networking Language • Universal Words (UWs) • Relations • Attributes • Knowledge Base

  4. forward(icl>send) @ entry @ past agt gol obj He(icl>person) minister(icl>person) @def mail(icl>collection) @def UNL Graph He forwarded the mail to the minister.

  5. AGT / AOJ / OBJ • AGT  (Agent)Definition:  Agt defines a thing which initiates an action • AOJ (Thing with attribute)Definition:  Aoj defines a thing which is in a state or has an attribute • OBJ (Affected thing)Definition: Obj defines a thing in focus which is directly affected by an event or state

  6. Examples • John broke the window. agt ( break.@entry.@past, John) • This flower is beautiful. aoj ( beautiful.@entry, flower) • He blamedJohn for the accident. obj ( blame.@entry.@past, John)

  7. BEN • BEN (Beneficiary)Definition:  Ben defines a not directly related beneficiary or victim of an event or state • Can I do anything for you? ben ( do.@entry.@interrogation.@politeness, you ) obj ( do.@entry.@interrogation.@politeness, anything ) agt (do.@entry.@interrogation.@politeness, I )

  8. PUR • PUR (Purpose or objective)Definition:  Pur defines thepurpose or objectives of the agent of an event or the purpose of a thing exist • This budget is for food. pur ( food.@entry, budget )mod ( budget, this )

  9. RSN • RSN (Reason)Definition:  Rsn defines a reason why an event or a state happens • They selected him for his honesty. agt(select(icl>choose).@entry, they) obj(select(icl>choose) .@entry, he) rsn (select(icl>choose).@entry, honesty)

  10. TIM • TIM (Time)Definition:  Tim defines the time an event occurs or a state is true • I wake up at noon. agt ( wake up.@entry, I )tim ( wake up.@entry, noon(icl>time))

  11. TMF • TMF (Initial time)Definition:  Tmf defines a time an event starts • The meeting started from morning. obj ( start.@entry.@past, meeting.@def )tmf ( start.@entry.@past, morning(icl>time) )

  12. TMT • TMT (Final time)Definition: Tmt defines a time an event ends • The meeting continued till evening. obj ( continue.@entry.@past, meeting.@def )tmt ( continue.@entry.@past,evening(icl>time) )

  13. PLC • PLC (Place)Definition:  Plc defines the place an event occurs or a state is true or a thing exists • He is very famous in India. aoj ( famous.@entry, he )man ( famous.@entry, very)plc ( famous.@entry, India)

  14. PLF • PLF  (Initial place)Definition:  Plf defines the place an event begins or a state becomes true • Participants come from the whole world. agt ( come.@entry, participant.@pl )plf ( come.@entry, world )mod ( world, whole)

  15. PLT • PLT  (Final place)Definition:  Plt defines the place an event ends or a state becomes false • We will go to Delhi. agt ( go.@entry.@future, we )plt ( go.@entry.@future, Delhi)

  16. INS • INS   (Instrument) Definition:  Ins defines the instrument to carry out an event • I solved it with computer agt ( solve.@entry.@past, I )ins ( solve.@entry.@past, computer )obj ( solve.@entry.@past, it )

  17. Attributes • Constitute syntax of UNL • Play the role of bridging the conceptual world and the real world in the UNL expressions • Show how and when the speaker views what is said and with what intention, feeling, and so on • Seven types: • Time with respect to the speaker • Aspects • Speaker’s view of reference • Speaker’s emphasis, focus, topic, etc. • Convention • Speaker’s attitudes • Speaker’s feelings and viewpoints

  18. Tense: @past He went there yesterday • The past tense is normally expressed by @past {unl} agt(go.@entry.@past, he) … {/unl}

  19. Aspects: @progress It’s raining hard. {unl} man ( rain.@entry.@present.@progress, hard ) {/unl}

  20. Speaker’s view of reference • @def (Specific concept (already referred)) The house on the corner is for sale. • @indef (Non-specific class) There is a book on the desk • @not is always attached to the UW which is negated. He didn’t come. agt ( come.@entry.@past.@not, he )

  21. Speaker’s emphasis • @emphasis John his name is. mod ( name, he )aoj ( John.@emphasis.@entry, name ) • @entry denotes the entry point or main UW of an UNL expression

  22. Subcategorization Frames • Specify the categorial class of the lexical item. • Specify the environment. • Examples: kick: [V; _ NP] cry: [V; _ ] rely: [V; _PP] put: [V; _ NP PP] think: : [V; _ S` ]

  23. Subcategorization Rules Subcategorization Rule: _NP] _ ] _PP] _NP PP] _S`] y / V

  24. Subcategorization Rules The boy relied on the friend. 1. S  NP VP 2. VP  V (NP) (PP) (S`)… 3. NP  Det N 4. V  rely / _PP] 5. P  on / _NP] 6. Det  the 7. N  boy, friend

  25. Semantically Odd Constructions • Can we exclude these two ill-formed structures ? • *The boy frightened sincerity. • *Sincerity kicked the boy. • Selectional Restrictions

  26. Selectional Restrictions • Inherent Properties of Nouns: [+/- ABSTRACT], [+/- ANIMATE] • E.g., Sincerity [+ ABSTRACT] Boy [+ANIMATE]

  27. __ [+/-ABSTARCT] V y / __ [+/-ANIMATE] __ V [+/-ABSTARCT] frighten / __ [+ANIMATE] Selectional Rules • A selectional rule specifies certain selectional restrictions associated with a verb.

  28. Subcategorization Frame forward e.g.,We will be forwarding our new catalogue to you V __ NP PP invitation e.g., An invitation to the party N __ PP accessible A e.g., A program making science is more accessible to young people __ PP

  29. forward V __ NP PP ( Event FORWARD [Thing THE MAN], [Thing THE MAIL], ) [Path TO THE MINISTER] Thematic Roles The man forwarded the mail to the minister.

  30. How to define the UWs in UNL Knowledge-Base? • Nominal concept • Abstract • Concrete • Verbal concept • Do • Occur • Be • Adjective concept • Adverbial concept

  31. Nominal Concept: Abstract thing abstract thing{(icl>thing)}culture(icl>abstract thing) civilization(icl>culture{>abstract thing}) direction(icl>abstract thing) east(icl>direction{>abstract thing})duty(icl>abstract thing) mission(icl>duty{>abstract thing}) responsibility(icl>duty{>abstract thing}) accountability{(icl>responsibility>duty)}event(icl>abstract thing{,icl>time>abstract thing}) meeting(icl>event{>abstract thing,icl>group>abstract thing}) conference(icl>meeting{>event}) TV conference{(icl>conference>meeting)}

  32. Nominal Concept: Concrete thing concrete thing{(icl>thing,icl>place>thing)} building(icl>concrete thing) factory(icl>building{>concrete thing}) house(icl>building{>concrete thing}) substance(icl>concrete thing) cloth(icl>substance{>concrete thing}) cotton(icl>cloth{>substance}) fiber(icl>substance{>concrete thing}) synthetic fiber{(icl>fiber>substance)} textile fiber{(icl>fiber>substance)} liquid(icl>substance{>concrete thing}) beverage(icl>food,icl>liquid>substance}) coffee(icl>beverage{>food}) liquor(icl>beverage{>food}) beer(icl>liquor{>beverage})

  33. Verbal concept: do do({icl>do,}agt>thing,gol>thing,obj>thing) express({icl>do(}agt>thing,gol>thing,obj>thing{)}) state(icl>express(agt>thing,gol>thing,obj>thing)) explain(icl>state(agt>thing,gol>thing,obj>thing)) add({icl>do(}agt>thing,gol>thing,obj>thing{)}) change({icl>do(}agt>thing,gol>thing,obj>thing{)}) convert(icl>change(agt>thing,gol>thing,obj>thing) classify({icl>do(}agt>thing,gol>thing,obj>thing{)}) divide(icl>classify(agt>thing,gol>thing,obj>thing))

  34. Verbal concept: occur and be • occur({icl>occur,}gol>thing,obj>thing) melt({icl>occur(}gol>thing,obj>thing{)}) divide({icl>occur(}gol>thing,obj>thing{)}) arrive({icl>occur(}obj>thing{)}) • be({icl>be,}aoj>thing{,^obj>thing}) exist({icl>be(}aoj>thing{)}) born({icl>be(}aoj>thing{)})

  35. How to define the UWs in UNL Knowledge Base? • In order to distinguish among the verb classes headed by 'do', 'occur' and 'be', the following features are used:

  36. How to define the UWs in UNL Knowledge-Base? • The verbal UWs (do, occur, be) also take some pre-defined semantic cases, as follows:

  37. Complex sentence I want to watch this movie. @entry.@past :01 watch (icl>do) @entry.@inf want (icl>) obj obj agt agt I (iof>person) movie(icl>) I (iof>person) @def

  38. Approach to UNL Generation

  39. Problem Definition • Generate UNL expressions for English sentences • in a robust and scalable manner, • using syntactic analysis and lexical resources extensively. • This needs • detecting semantically relatable entities • and solving attachment problems

  40. Semantically Relatable Sequences (SRS) Definition: A semantically relatable Sequence (SRS) of a sentence is a group of words in the sentence (not necessarily consecutive) that appear in the semantic graph of the sentence as linked nodes or nodes with speech act labels (This is motivated by UNL representation)

  41. Source Language Sentence Target Language Sentence SRS UNL SRS as an intermediary to and intermediary

  42. past tense bought time agent object man June car the: definite in: modifier a: indefinite modifier new Example to illustrate SRS “The man bought a new car in June”

  43. Sequences from “the man bought a new car in June” • {man, bought} • {bought, car} • {bought, in, June} • {new, car} • {the, man} • {a, car}

  44. Basic questions • Which words can form semantic constituents, which we call Semantically Relatable Sequences (SRS)? • What after all are the SRSs of the given sentence? • What semantic relations can link the words in an SRS and the SRSs themselves?

  45. Postulate • A sentence needs to be broken into Sequences of at most three forms • {CW, CW} • {CW, FW, CW} • {FW, CW} where CW refers to content word or a clause and FW to function word

  46. SRS and Language Phenomena

  47. Movement: Preposition Stranding • John, we laughed at. • (we , laughed.@entry)---------(CW, CW) • (laughed.@entry,at, John)---(CW, FW, CW)

  48. Movement: Topicalization • The problem, we solved. • (we , solved.@entry)------------(CW, CW) • (solved.@entry , problem)-----(CW,CW) • (the, problem)--------------------(CW,CW)

  49. Movement: Relative Clauses • John told a joke which we had already heard. • (John, told.@entry) -------------------(CW, CW) • (told.@entry, :01) ---------------------(CW,CW) • SCOPE01(we,had,heard.@entry)-------(CW, FW,CW) • SCOPE01(already,heard.@entry)-------(CW,CW) • SCOPE01(heard@entry,which,joke)----(CW,FW,CW) • SCOPE01(a, joke)--------------------------(FW,CW)

  50. Movement: Interrogatives • Who did you refer her to? • (did , refer.@entry.@interrogative)-------(FW,CW) • (you, refer.@entry.@interrogative)--------(CW,CW) • (refer.@entry.@interrogative , her)--------(CW,CW) • (refer.@entry.@interrogative , to,who)---- (CW,FW,CW)

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