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Recursive Descent Parsing and CYK ANLP: Lecture 13 Shay Cohen 14 October 2019 1 / 1 Last Class Chomsky normal form grammars English syntax Agreement phenomena and the way to model them with CFGs 2 / 1 Recap: Syntax Two reasons to care

  1. Algorithm Sketch: Shift-Reduce Parsing Until the words in the sentences are substituted with S: ◮ Scan through the input until we recognise something that corresponds to the RHS of one of the production rules (shift) ◮ Apply a production rule in reverse; i.e., replace the RHS of the rule which appears in the sentential form with the LHS of the rule (reduce) A shift-reduce parser implemented using a stack: 1. start with an empty stack 2. a shift action pushes the current input symbol onto the stack 3. a reduce action replaces n items with a single item 37 / 1

  2. Shift-Reduce Parsing Stack Remaining Text my dog saw a man in the park with a statue 38 / 1

  3. Shift-Reduce Parsing Stack Remaining Text Det dog saw a man in the park with a statue my 39 / 1

  4. Shift-Reduce Parsing Stack Remaining Text Det N saw a man in the park with a statue my dog 40 / 1

  5. Shift-Reduce Parsing Stack Remaining Text NP saw a man in the park with a statue Det N my dog 41 / 1

  6. Shift-Reduce Parsing Stack Remaining Text NP V NP in the park with a statue Det N saw Det N my dog a man 42 / 1

  7. Shift-Reduce Parsing Stack Remaining Text NP V NP PP with a statue Det N saw Det N P NP my dog a man in Det N the park 43 / 1

  8. Shift-Reduce Parsing Stack NP V NP Det N saw NP PP my dog Det N P NP a man in Det N the park 44 / 1

  9. Shift-Reduce Parsing Stack NP VP Det N V NP my dog saw NP PP Det N P NP a man in Det N the park 45 / 1

  10. Shift-Reduce Parsing Stack S NP VP Det N V NP my dog saw NP PP Det N P NP a man in Det N the park 46 / 1

  11. How many parses are there? If our grammar is ambiguous (inherently, or by design) then how many possible parses are there? 47 / 1

  12. How many parses are there? If our grammar is ambiguous (inherently, or by design) then how many possible parses are there? In general: an infinite number, if we allow unary recursion. 47 / 1

  13. How many parses are there? If our grammar is ambiguous (inherently, or by design) then how many possible parses are there? In general: an infinite number, if we allow unary recursion. More specific: suppose that we have a grammar in Chomsky normal form. How many possible parses are there for a sentence of n words? Imagine that every nonterminal can rewrite as every pair of nonterminals (A → BC) and every nonterminal (A → a) 1. n 2. n 2 3. n log n (2 n )! 4. ( n +1)! n ! 47 / 1

  14. How many parses are there? A a a 48 / 1

  15. How many parses are there? A a a A A A A A A a a a a a a 48 / 1

  16. How many parses are there? A a a A A A A A A a a a a a a A A A A A A A A a a a a a a a a 48 / 1

  17. How many parses are there? A a a A A A A A A a a a a a a A A A A A A A A A A A A A A A A a a a a a a a a a a a a a a a a 48 / 1

  18. How many parses are there? A a a A A A A A A a a a a a a A A A A A A A A A A A A A A A A A A A a a a a a a a a a a a a a a a a a a a a 48 / 1

  19. How many parses are there? Intution. Let C ( n ) be the number of binary trees over a sentence of length n . The root of this tree has two subtrees: one over k words (1 ≤ k < n ), and one over n − k words. Hence, for all values of k , we can combine any subtree over k words with any subtree over n − k words: n − 1 � C ( n ) = C ( k ) × C ( n − k ) k =1 49 / 1

  20. How many parses are there? Intution. Let C ( n ) be the number of binary trees over a sentence of length n . The root of this tree has two subtrees: one over k words (1 ≤ k < n ), and one over n − k words. Hence, for all values of k , we can combine any subtree over k words with any subtree over n − k words: n − 1 � C ( n ) = C ( k ) × C ( n − k ) k =1 (2 n )! C ( n ) = ( n + 1)! n ! These numbers are called the Catalan numbers. They’re big numbers! n 1 2 3 4 5 6 8 9 10 11 12 C ( n ) 1 1 2 5 14 42 132 429 1430 4862 16796 49 / 1

  21. Problems with Parsing as Search 1. A recursive descent parser (top-down) will do badly if there are many different rules for the same LHS. Hopeless for rewriting parts of speech (preterminals) with words (terminals). 2. A shift-reduce parser (bottom-up) does a lot of useless work: many phrase structures will be locally possible, but globally impossible. Also inefficient when there is much lexical ambiguity. 3. Both strategies do repeated work by re-analyzing the same substring many times. We will see how chart parsing solves the re-parsing problem, and also copes well with ambiguity. 50 / 1

  22. Dynamic Programming With a CFG, a parser should be able to avoid re-analyzing sub-strings because the analysis of any sub-string is independent of the rest of the parse. NP The dog saw a man in the park NP NP NP The parser’s exploration of its search space can exploit this independence if the parser uses dynamic programming. Dynamic programming is the basis for all chart parsing algorithms. 51 / 1

  23. Parsing as Dynamic Programming ◮ Given a problem, systematically fill a table of solutions to sub-problems: this is called memoization. ◮ Once solutions to all sub-problems have been accumulated, solve the overall problem by composing them. ◮ For parsing, the sub-problems are analyses of sub-strings and correspond to constituents that have been found. ◮ Sub-trees are stored in a chart (aka well-formed substring table), which is a record of all the substructures that have ever been built during the parse. Solves re-parsing problem : sub-trees are looked up, not re-parsed! Solves ambiguity problem : chart implicitly stores all parses! 52 / 1

  24. Depicting a Chart A chart can be depicted as a matrix: ◮ Rows and columns of the matrix correspond to the start and end positions of a span (ie, starting right before the first word, ending right after the final one); ◮ A cell in the matrix corresponds to the sub-string that starts at the row index and ends at the column index. ◮ It can contain information about the type of constituent (or constituents) that span(s) the substring, pointers to its sub-constituents, and/or predictions about what constituents might follow the substring. 53 / 1

  25. CYK Algorithm CYK (Cocke, Younger, Kasami) is an algorithm for recognizing and recording constituents in the chart. ◮ Assumes that the grammar is in Chomsky Normal Form: rules all have form A → BC or A → w . ◮ Conversion to CNF can be done automatically. NP → Det Nom NP → Det Nom Nom → N | OptAP Nom Nom → book | orange | AP Nom OptAP → | OptAdv A AP → heavy | orange | Adv A ǫ A → heavy | orange A → heavy | orange Det → a Det → a OptAdv → | very Adv → very ǫ N → | orange book 54 / 1

  26. CYK: an example Let’s look at a simple example before we explain the general case. Grammar Rules in CNF NP → Det Nom Nom → | orange | AP Nom book AP → | orange | Adv A heavy A → heavy | orange Det → a Adv → very (N.B. Converting to CNF sometimes breeds duplication!) Now let’s parse: a very heavy orange book 55 / 1

  27. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a 1 very 2 heavy 3 orange 4 book 56 / 1

  28. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very 2 heavy 3 orange 4 book 56 / 1

  29. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv 2 heavy 3 orange 4 book 56 / 1

  30. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv 2 heavy A,AP 3 orange 4 book 56 / 1

  31. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv AP 2 heavy A,AP 3 orange 4 book 56 / 1

  32. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv AP 2 heavy A,AP 3 orange Nom,A,AP 4 book 56 / 1

  33. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv AP 2 heavy A,AP Nom 3 orange Nom,A,AP 4 book 56 / 1

  34. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det 1 very Adv AP Nom 2 heavy A,AP Nom 3 orange Nom,A,AP 4 book 56 / 1

  35. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP 1 very Adv AP Nom 2 heavy A,AP Nom 3 orange Nom,A,AP 4 book 56 / 1

  36. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP 1 very Adv AP Nom 2 heavy A,AP Nom 3 orange Nom,A,AP 4 book Nom 56 / 1

  37. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP 1 very Adv AP Nom 2 heavy A,AP Nom 3 orange Nom,A,AP Nom 4 book Nom 56 / 1

  38. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP 1 very Adv AP Nom 2 heavy A,AP Nom Nom 3 orange Nom,A,AP Nom 4 book Nom 56 / 1

  39. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP 1 very Adv AP Nom Nom 2 heavy A,AP Nom Nom 3 orange Nom,A,AP Nom 4 book Nom 56 / 1

  40. Filling out the CYK chart 0 a 1 very 2 heavy 3 orange 4 book 5 1 2 3 4 5 a very heavy orange book 0 a Det NP NP 1 very Adv AP Nom Nom 2 heavy A,AP Nom Nom 3 orange Nom,A,AP Nom 4 book Nom 56 / 1

  41. CYK: The general algorithm function C KY-Parse( words , grammar ) returns table for j ← from 1 to Length ( words ) do table [ j − 1 , j ] ← { A | A → words [ j ] ∈ grammar } for i ← from j − 2 downto 0 do for k ← i + 1 to j − 1 do table [ i , j ] ← table [ i , j ] ∪ { A | A → BC ∈ grammar , B ∈ table [ i , k ] C ∈ table [ k , j ] } 57 / 1

  42. CYK: The general algorithm function C KY-Parse( words , grammar ) returns table for j ← from 1 to Length ( words ) do loop over the columns table [ j − 1 , j ] ← { A | A → words [ j ] ∈ grammar } fill bottom cell for i ← from j − 2 downto 0 do fill row i in column j for k ← i + 1 to j − 1 do loop over split locations table [ i , j ] ← table [ i , j ] ∪ between i and j { A | A → BC ∈ grammar , Check the grammar B ∈ table [ i , k ] for rules that C ∈ table [ k , j ] } link the constituents in [ i , k ] with those in [ k , j ]. For each rule found store LHS in cell [ i , j ]. 58 / 1

  43. A succinct representation of CKY We have a Boolean table called Chart , such that Chart [ A , i , j ] is true if there is a sub-phrase according the grammar that dominates words i through words j Build this chart recursively, similarly to the Viterbi algorithm: For j > i + 1: j − 1 � � Chart [ A , i , j ] = Chart [ B , i , k ] ∧ Chart [ C , k , j ] k = i +1 A → B C Seed the chart, for i + 1 = j : Chart [ A , i , i + 1] = True if there exists a rule A → w i +1 where w i +1 is the ( i + 1)th word in the string 59 / 1

  44. From CYK Recognizer to CYK Parser ◮ So far, we just have a chart recognizer, a way of determining whether a string belongs to the given language. ◮ Changing this to a parser requires recording which existing constituents were combined to make each new constituent. ◮ This requires another field to record the one or more ways in which a constituent spanning (i,j) can be made from constituents spanning (i,k) and (k,j). (More clearly displayed in graph representation, see next lecture.) ◮ In any case, for a fixed grammar, the CYK algorithm runs in time O ( n 3 ) on an input string of n tokens. ◮ The algorithm identifies all possible parses. 60 / 1

  45. CYK-style parse charts Even without converting a grammar to CNF, we can draw CYK-style parse charts: 1 2 3 4 5 a very heavy orange book 0 a Det NP NP 1 very OptAdv OptAP Nom Nom 2 heavy A,OptAP Nom Nom 3 orange N,Nom,A,AP Nom 4 book N,Nom (We haven’t attempted to show ǫ -phrases here. Could in principle use cells below the main diagonal for this . . . ) However, CYK-style parsing will have run-time worse than O ( n 3 ) if e.g. the grammar has rules A → BCD . 61 / 1

  46. Second example Grammar Rules in CNF S → NP VP Nominal → book | flight | money S → X 1 VP Nominal → Nominal noun X 1 → Aux VP Nominal → Nominal PP S → book | include | prefer VP → book | include | prefer S → Verb NP VPVerb → NP S → X 2 VP → X 2 PP S → Verb PP X 2 → Verb NP S → VP PP VP → Verb NP NP → TWA | Houston VP → VP PP NP → Det Nominal PP → Preposition NP Verb → book | include | prefer Noun → book | flight | money Let’s parse Book the flight through Houston ! 62 / 1

  47. Second example Grammar Rules in CNF S → NP VP Nominal → book | flight | money S → X 1 VP Nominal → Nominal noun X 1 → Aux VP Nominal → Nominal PP S → book | include | prefer VP → book | include | prefer S → Verb NP VPVerb → NP S → X 2 VP → X 2 PP S → Verb PP X 2 → Verb NP S → VP PP VP → Verb NP NP → TWA | Houston VP → VP PP NP → Det Nominal PP → Preposition NP Verb → book | include | prefer Noun → book | flight | money Let’s parse Book the flight through Houston ! 62 / 1

  48. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] 63 / 1

  49. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] Det [1 , 2] 63 / 1

  50. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] Det [1 , 2] Nominal, Noun [2 , 3] 63 / 1

  51. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] Det [1 , 2] Nominal, Noun [2 , 3] Prep [3 , 4] 63 / 1

  52. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] Det [1 , 2] Nominal, Noun [2 , 3] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  53. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] [0 , 2] Det [1 , 2] Nominal, Noun [2 , 3] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  54. Second example Book the flight through Houston S, VP, Verb, Nominal, Noun [0 , 1] [0 , 2] Det NP [1 , 2] [1 , 3] Nominal, Noun [2 , 3] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  55. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] Det NP [1 , 2] [1 , 3] Nominal, Noun [2 , 3] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  56. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] Det NP [1 , 2] [1 , 3] Nominal, Noun [2 , 3] [2 , 4] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  57. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] Det NP [1 , 2] [1 , 3] [1 , 4] Nominal, Noun [2 , 3] [2 , 4] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  58. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] [0 , 4] Det NP [1 , 2] [1 , 3] [1 , 4] Nominal, Noun [2 , 3] [2 , 4] Prep [3 , 4] NP, Proper- Noun [4 , 5] 63 / 1

  59. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] [0 , 4] Det NP [1 , 2] [1 , 3] [1 , 4] Nominal, Noun [2 , 3] [2 , 4] Prep PP [3 , 4] [3 , 5] NP, Proper- Noun [4 , 5] 63 / 1

  60. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] [0 , 4] Det NP [1 , 2] [1 , 3] [1 , 4] Nominal, Nominal Noun [2 , 3] [2 , 4] [2 , 5] Prep PP [3 , 4] [3 , 5] NP, Proper- Noun [4 , 5] 63 / 1

  61. Second example Book the flight through Houston S, S, VP, Verb, VP, Nominal, X2 Noun [0 , 1] [0 , 2] [0 , 3] [0 , 4] Det NP NP [1 , 2] [1 , 3] [1 , 4] [1 , 5] Nominal, Nominal Noun [2 , 3] [2 , 4] [2 , 5] Prep PP [3 , 4] [3 , 5] NP, Proper- Noun [4 , 5] 63 / 1

  62. Second example Book the flight through Houston S, S 1 , VP, X2, S, VP, Verb, VP, S 2 , VP, Nominal, X2 S 3 Noun [0 , 1] [0 , 2] [0 , 3] [0 , 4] [0 , 5] Det NP NP [1 , 2] [1 , 3] [1 , 4] [1 , 5] Nominal, Nominal Noun [2 , 3] [2 , 4] [2 , 5] Prep PP [3 , 4] [3 , 5] NP, Proper- Noun [4 , 5] 63 / 1

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