1. 8 November 2003 PP attachment problem1 Prepositional Phrase Attachment Problem 03M05601 Ashish Almeida

2. 8 November 2003 PP attachment problem2 Overview –Introduction to NLP –Analysis in UNL system –Prepositional phrase attachment problem –Proposed method to handle this problem

3. 8 November 2003 PP attachment problem3 Motivation Analysis involves many complex problems Prepositional phrase attachment problem is one such difficult problem. If solved, improve the quality of information extracted manifold No existing system solves the problem

4. 8 November 2003 PP attachment problem4 Tasks involved in NLP Analysis and generation TextMeaning NL understanding NL generation

5. 8 November 2003 PP attachment problem5 Phases in NLP Morphological analysis Syntactic analysis Semantic analysis Discourse integration Pragmatic analysis

6. 8 November 2003 PP attachment problem6 Is NL Compositional ? Compsitional expression –Meaning of the whole from meaning of parts e.g.strong tea - rich tea day by day - all the time

7. 8 November 2003 PP attachment problem7 Analysis Morphological + Syntactic + Semantic analysis All these phases are dependent on each other. Interactive Vs modular approach Analysis in UNL system - interactive

8. 8 November 2003 PP attachment problem8 UNL … UNL is Interlingua e.g. Ram ate rice with spoon. agt obj spoon (icl>artifact) John (iof>person) rice (icl>food) eat (icl>do) @ entry @ present ins

9. 8 November 2003 PP attachment problem9 UNL expresion UNL Expression for Ram ate rice with spoon. agt(eat(icl>do).@past.@entry, Ram(iof>person)) obj(eat(icl>do).@past.@entry, rice(icl>food)) ins(eat(icl>do).@past.@entry, spoon(icl>tool)) Relation AttributesUWs agt(eat(icl>do).@past.@entry, Ram(iof>person))

10. 8 November 2003 PP attachment problem10 Analysis in UNL Enconverter –Natural Language to UNL –Handles one sentence at a time –Predicate preserving parser –Kind of Turing machine Components –Dictionary : lexical units, uw, semantic attributes –Rule base : head movement rules, relation resolving rules Working –Uses dictionary and rule bases to process the sentence.

11. 8 November 2003 PP attachment problem11 Prepositional Phrase Attachment Problem Type of Structural ambiguity in a sentence on new technologies. PP the report NP read VP John NP Verb attachment Noun attachment

12. 8 November 2003 PP attachment problem12 Prepositional Phrase Attachment Problem… Noun attachment Vs verb attachment e.g. John read the report on new technologies. read Johnthe report on new technologies read John the report on new technologies *

13. 8 November 2003 PP attachment problem13 Establishing semantic relation Same structure-different semantic relation e.g. 1. Ram ate rice with spoon.……instrument The UNL for this sentence is ins(eat(icl>do).@past.@entry, spoon(icl>tool)) 2. Ram ate rice with Sita.……co-agent The UNL for this sentence is cag(eat(icl>do).@past.@entry, Sita(iof>person))

14. 8 November 2003 PP attachment problem14 Difficult problem PP attachment problem is simpler or no problem for human being - who use world knowledge to process it. This world knowledge is not available to machines. e.g. travel by night …time travel by bus …instrument

15. 8 November 2003 PP attachment problem15 Different sites of attachment –The search for the policy is going on. –The test will be held at the end of August. –In August 1947, India became free from British rule. –Wilson received a medal from the commanding officer at a farewell party. There is no restriction on how far the PP can lie from the word to which it relates.

16. 8 November 2003 PP attachment problem16 Affinity with preceding phrase The preposition of gets attached to a noun phrase or a verb phrase immediately preceding it. –They were involved in the murder of a 90-year-old woman. –It was begun last week by the crew of a giant crane-barge. –He died of an overdose of sleeping pills –The system will be tailored to meet the need of the political party.

17. 8 November 2003 PP attachment problem17 Existing methods generate mod-obj combination for almost all PP relations –E.g He came according to his promise. agt(come(icl>do)@past.@entry, he) *mod(come(icl>do)@past.@entry, :01) obj:01(according to, promise(icl>abstract thing)) mod:01(promise(icl>abstract thing),he) Tags introduced manually to resolve phrase boundaries –E.g. It delineates the scope of phrases before conversion of the sentence.

18. 8 November 2003 PP attachment problem18 Related work Statistical learning methods used Wordnet is used to find relations between words Analysis of corpus is required Not all aspects of problem considered The hypothesis does not apply to all cases “PP attachments obey the principle of locality”

19. 8 November 2003 PP attachment problem19 Observations Prepositions frequency is calculated from British National Corpus Classified into 2 parts –Simple Preposition –Ambiguous prepositions FrequencyPrepositionPoly. count 29391of7 18214in10 9343to8 14by way of1 16by means of1

20. 8 November 2003 PP attachment problem20 Addition to Semantic Attributes hierarchy Semantic attributes required to disambiguate Addition required, if existing attributes fail to classify necessary condition –the attributes should be able to classify the semantically separate structures as separate entities. e.g. the train for Delhi….to() the price for the Hill Road pool….mod()

21. 8 November 2003 PP attachment problem21 Inclusion of preposition in UNL expression a picture on the wall plc(picture, wall). The cat walked across the street. –Wrong UNL *plc ( walk, street ) -cat walked along the street -cat walked across the street –Correct UNL plc (walk, :01) obj:01(across, street)

22. 8 November 2003 PP attachment problem22 Classification based on syntax structure Sentences have different syntactic structure Parsing the depends on surface structure - Active-passive, transitive-di-transitive, present-past participles etc. [ Verb + for + Noun phrase] v-purHe was waiting for the rainy day. v-purHe applied for a certificate. [ Noun phrase + for + Noun phrase] n-modThe search for the policy is going on. n-modHe pays the price for his indulgence. Classification based on syntax pattern

23. 8 November 2003 PP attachment problem23 Classification based on semantics Deciding factors –Syntax, attributes, preposition, subcategorisation frame(for verbs) Partial list of preposition on and its possible semantic relation RelationExample sentence ON plca picture on a wall insto travel on the bus timHe came on Sunday seqReport to reception on arrival moda book on South Africa insShe played a tune on her guitar plcYou can get me on 0181 530 3906

24. 8 November 2003 PP attachment problem24 Updating rule base Simpler if the classification is perfect. Issues involved –Priority, proper specification Two rules showing difference in priority – specific to general Comment;N/abs for N/abs ;search for policy delete preposition for DL(N,ABS) {PRE,#FOR:::} {N,ABS:+PRERES,+FORRES,+pPUR::}P25; Comment;V FOR N-UNIT-QUARES ;suspend for 2 days Delete preposition for DL(VRB){PRE,#FOR:::} {N,UNIT,TIM,QUARES :+PRERES,+FORRES,+pDUR::}P30;

25. 8 November 2003 PP attachment problem25 Conclusion World knowledge is realized in terms of semantic attributes. Phrasal verbs are not considered Idiomatic constructs are not handled - e.g. day by day all the time