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Pushpak Bhattacharyya CSE Dept., IIT Bombay 21 st March, 2011

CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 28– Grammar; Constituency, Dependency). Pushpak Bhattacharyya CSE Dept., IIT Bombay 21 st March, 2011. Grammar. A finite set of rules that generates only and all sentences of a language.

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Pushpak Bhattacharyya CSE Dept., IIT Bombay 21 st March, 2011

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  1. CS460/626 : Natural Language Processing/Speech, NLP and the Web(Lecture 28– Grammar; Constituency, Dependency) Pushpak BhattacharyyaCSE Dept., IIT Bombay 21st March, 2011

  2. Grammar • A finite set of rules • that generates only and all sentences of a language. • that assigns an appropriate structural description to each one.

  3. Grammatical Analysis Techniques Labeling the Constituents • Two main devices Breaking up a String • Sequential • Hierarchical • Transformational • Morphological • Categorial • Functional

  4. Breaking up and Labeling • Sequential Breaking up • Sequential Breaking up and Morphological Labeling • Sequential Breaking up and Categorial Labeling • Sequential Breaking up and Functional Labeling • Hierarchical Breaking up • Hierarchical Breaking up and Categorial Labeling • Hierarchical Breaking up and Functional Labeling

  5. Sequential Breaking up • That student solved the problems. student that + + solve + + + + s ed the problem

  6. Sequential Breaking up and Morphological Labeling • That student solved the problems. student that solve s ed the problem word word stem affix affix word stem

  7. Sequential Breaking up and Categorial Labeling • This boy can solve the problem. boy this can solve the problem N Det Aux Det V N • They called her a taxi. They call ed her a taxi Pron V Pron Det N Affix

  8. Sequential Breaking up and Functional Labeling They called her a taxi Subject Verbal Direct Object Indirect Object her a taxi They called Indirect Object Direct Object Subject Verbal

  9. Hierarchical Breaking up • Old men and women Old men and women Old men and women men and women and women Old men Old men and women Old men

  10. Hierarchical Breaking up and Categorial Labeling • Poor John ran away. S NP VP A N V Adv Poor John ran away

  11. Hierarchical Breaking up and Functional Labeling • Immediate Constituent (IC) Analysis • Construction types in terms of the function of the constituents: • Predication (subject + predicate) • Modification (modifier + head) • Complementation (verbal + complement) • Subordination (subordinator + dependent unit) • Coordination (independent unit + coordinator)

  12. Predication • [Birds]subject [fly]predicate S Subject Predicate Birds fly

  13. Modification • [A]modifier [flower]head • John [slept]head [in the room]modifier S Subject Predicate John Head Modifier In the room slept

  14. Complementation • He [saw]verbal [a lake]complement S Subject Predicate He Verbal Complement saw alake ICON 2004 Tutorial

  15. Subordination • John slept [in]subordinator [the room]dependentunit S Subject Predicate John Head Modifier Subordinator Dependent Unit slept in theroom

  16. Coordination • [John came in time] independent unit [but]coordinator [Mary was not ready] independent unit S Independent Unit Independent Unit Coordinator John came in time but Mary was not ready

  17. An Example S • In the morning, the sky looked much brighter. Modifier Head Subject Predicate Subordinator DU Modifier Modifier Head Head Verbal Complement Modifier Head In the morning, the sky looked much brighter

  18. Hierarchical Breaking up and Categorial / Functional Labeling • Hierarchical Breaking up coupled with Categorial /Functional Labeling is a very powerful device. • But there are ambiguities which demand something more powerful. • E.g., Love of God • Someone loves God • God loves someone

  19. Hierarchical Breaking up Categorial Labeling Functional Labeling Love of God Love of God Head Modifier Noun Phrase Prepositional Phrase Sub DU love of God love of God

  20. Types of Generative Grammar • Finite State Model (sequential) • Phrase Structure Model (sequential + hierarchical) + (categorial) • Transformational Model (sequential + hierarchical + transformational) + (categorial + functional)

  21. Phrase Structure Grammar (PSG) A phrase-structure grammar G consists of a four tuple (V, T, S, P), where • V is a finite set of alphabets (or vocabulary) • E.g., N, V, A, Adv, P, NP, VP, AP, AdvP, PP, student, sing, etc. • T is a finite set of terminal symbols: T  V • E.g., student, sing,etc. • S is a distinguished non-terminal symbol, also called start symbol: S  V • P is a set of productions.

  22. Noun Phrases • John • the student • the intelligent student NP NP NP N N N Det Det AdjP student John student the the intelligent

  23. Noun Phrase • his first five PhD students NP N Quant Det Ord N students five his first PhD

  24. Noun Phrase • The five best students of my class NP PP Quant Det AP N five the best students of my class

  25. Verb Phrases • can sing • can hit the ball VP VP V Aux NP Aux V can sing the ball can hit

  26. Verb Phrase • Can give a flower to Mary VP NP Aux V PP a flower can give to Mary

  27. Verb Phrase • may make John the chairman VP NP Aux V NP John may make thechairman

  28. Verb Phrase • may find the book very interesting VP NP Aux V AP veryinteresting thebook may find

  29. Prepositional Phrases • in the classroom • near the river PP PP NP NP P P in near theclassroom theriver

  30. Adjective Phrases • intelligent • very honest • fond of sweets AP AP AP A PP Degree A A very fond honest ofsweets intelligent

  31. Adjective Phrase • very worried that she might have done badly in the assignment AP Degree A S’ very worried that she might have done badly in the assignment

  32. Phrase Structure Rules • The boy hit the ball. • Rewrite Rules: (i) S  NP VP (ii) NP Det N (iii) VP  V NP • Det the • N man, ball • V hit • We interpret each rule X Yas the instruction rewrite X as Y.

  33. Derivation • The boy hit the ball. • Sentence NP + VP (i) Det + N + VP (ii) Det + N + V + NP (iii) The + N + V + NP (iv) The + boy + V + NP (v) The + boy + hit + NP (vi) The + boy + hit + Det + N (ii) The + boy + hit + the + N (iv) The + boy + hit + the + ball(v)

  34. PSG Parse Tree • The boy hit the ball. S NP VP Det N V NP the Det N boy hit the ball

  35. PSG Parse Tree S • John wrote those words in the Book of Proverbs. NP VP PropN PP V NP NP P NP PP John in thosewords wrote ofproverbs thebook

  36. Penn POS Tags • John wrote those words in the Book of Proverbs. [John/NNP ] wrote/VBD [ those/DT words/NNS ] in/IN [ the/DT Book/NN ] of/IN [ Proverbs/NNS ]

  37. Penn Treebank • John wrote those words in the Book of Proverbs. (S (NP-SBJ (NP John)) (VP wrote (NP those words) (PP-LOC in (NP (NP-TTL (NP the Book) (PP of (NP Proverbs)))

  38. PSG Parse Tree S • Official trading in the shares will start in Paris on Nov 6. NP VP NP PP Aux V PP PP NP N P AP A will start onNov6 inParis trading official in theshares

  39. Penn POS Tags • Official trading in the shares will start in Paris on Nov 6. [ Official/JJ trading/NN ] in/IN [ the/DT shares/NNS ] will/MD start/VB in/IN [ Paris/NNP ] on/IN [ Nov./NNP 6/CD ]

  40. Penn Treebank • Official trading in the shares will start in Paris on Nov 6. ( (S (NP-SBJ (NP Official trading) (PP in (NP the shares))) (VP will (VP start (PP-LOC in (NP Paris)) (PP-TMP on (NP (NP Nov 6)

  41. Penn POS Tag Sset • Adjective: JJ • Adverb: RB • Cardinal Number: CD • Determiner: DT • Preposition: IN • Coordinating Conjunction CC • Subordinating Conjunction: IN • Singular Noun: NN • Plural Noun: NNS • Personal Pronoun: PP • Proper Noun: NP • Verb base form: VB • Modal verb: MD • Verb (3sg Pres): VBZ • Wh-determiner: WDT • Wh-pronoun: WP

  42. Difference between constituency and dependency

  43. Constituency Grammar • Categorical • Uses part of speech • Context Free Grammar (CFG) • Basic elements  Phrases

  44. Dependency Grammar • Functional • Context Free Grammar • Basic elements  Units of Predication/ Modification/ Complementation/ Subordination/ Co-ordination

  45. Bridge between Constituency and Dependency parse • Constituency uses phrases • Dependencies consist of Head-modifier combination • This is a cricket bat. • Cricket (Category: N, Functional: Adj) • Bat (Category: N, Functional: N) • For languages which are free word order we use dependency parser to uncover the relations between the words. • Raam ne Shaamkodekha. (Ram saw Shyam) • Shaamko Ram ne dekha. (Ram saw Shyam) • Case markers cling to the nouns they subordinate

  46. Example of CG and DG output Birds Fly. S S NP VP Subject Predicate N V Birds fly Birds fly

  47. Some probabilistic parsers and why they are used • Stanford, Collins, Charniack, RASP • Why Probabilistic parsers • For a single sentence we can have multiple parses. • Probability for the parse is calculated and then the parse with the highest probability is selected. • This is needed in many applications of NLP, that need parsing.

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