# Constituent structure and structural ambiguity. Feb. 16, 2006 Nianwen Xue

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1 Constituent structure and structural ambiguity Feb. 16, 2006 Nianwen Xue

2 Outline Intuitions and tests for constituent structure Representing constituent structures Continuous constituents Discontinuous constituents Types of traces Structural ambiguity resolution PP attachment Human and computer perspectives

3 Some words belong together Did [the dog] the children like chase the cat? Did the [dog the] children like chase the cat?

4 Do we have evidence for that? Did the dog chase the cat? Did she chase him? Did the dog the children like chase the cat? Did the dog they like chase the cat? Did she they like chase the cat?

5 Substitution test Replace the constituent with a pro-form The little boy fed the cat. He fed her. The little boy from next door fed the cat without a tail. He fed her. The little boy from next door fed the cat without a tail. * He from next door fed her without a tail.

6 Substitution test Black cats detest green peas. They detest them. These black cats detest those green peas. They detest them. These black cats detest those green peas. * These they detest those them. Assumption: only constituents can be substituted with proforms

7 Pronouns are not the only proforms Put it on the table. Put it there. Put it over on the table. Put it over there. Put it over on the table. Put it there. Put it on the table that's by the door. * Put it there that's by the door. Put it over on the table that's by the door. * Put it over there that's by the door. Put it over on the table that's by the door. * Put it there that's by the door.

8 Pro-adjective I am very happy, and Linda is so, too. I am very fond of Lukas, and Linda is so, too. I am very fond of my nephew,... *... and Linda is so of her niece.

9 Pro-clause? I { know, suspect } that they're invited. I { know, suspect } it. I { imagine, think } that they're invited. I { imagine, think } so.

10 Moving s I fed the cats. The cats, I fed. (The dogs, I didn't.) I fed the cats with long, fluffy tails. The cats with long, fluffy tails, I fed. (The other cats, I didn't.) * The cats, I fed with long, fluffy tails. Assumption: only constituents can be moved

11 Moving PP, ADJP, The cat strolled across the porch with a confident air. With a confident air, the cat strolled across the porch. * With a, the cat strolled across the porch confident air. Ali Baba returned from his travels wiser than before. Wiser than before, Ali Baba returned from his travels. *Wiser than, Ali Baba returned from his travels before.

12 Moving AD They arrived at the concert hall more quickly than they had expected. More quickly than they had expected, they arrived at the concert hall. * More quickly than they, they arrived at the concert hall had expected.

13 Can it be a sentence fragment in response to a question? Noun phrase: What do you like? The cats. Cats with long, fluffy tails. The cats with long, fluffy tails. Prepositional phrase: How did the cat stroll across the porch? With a confident air. Where did Ali Baba go? On a long journey. To New York.

14 Can it be a sentence fragment in response to a question? Adjective phrase: How did Ali Baba return? Wiser than before. Fairly jeg-lagged. Adverb phrase: How did they do? Not badly. Surprisingly well. Much better than they had expected.

15 Ungrammatical with nonconstituents * What did you feed long, fluffy tails? * The cats with. * How did the cat stroll across the porch confident air? * With a. * How did Ali Baba return from his travels before? * Wiser than. * How did they arrive at the concert hall had expected? * More quickly than they.

16 It cleft focus Noun phrase Ordinary cats detest the smell of citrus fruits. It is ordinary cats that detest the smell of citrus fruits. Prepositional phrase The cat strolled across the porch with a confident air. It was with a confident air that the cat strolled across the porch. Adjective phrase Ali Baba returned from his travels wiser than before. It was wiser than before that Ali Baba returned from his travels. Adverb phrase They arrived at the concert hall more quickly than they had expected. It was more quickly than they had expected that they arrived at

17 Ungrammatical it cleft Ordinary cats detest the smell of citrus fruits. It is the smell of that ordinary cats detest citrus fruits. The cat strolled across the porch with a confident air. * It was with a confident that the cat strolled across the porch air. Ali Baba returned from his travels wiser than before. * It was wiser than that Ali Baba returned from his travels before. They arrived at the concert hall more quickly than they had expected. * It was more quickly than that they arrived at the concert hall they has expected.

18 Representing constituent structure Labeled brackets The label represents the category of the constituent The text string within a bracket represents a constituent Preferred representation scheme in corpus linguistics: bracket bank? Tree diagram Syntactic constituents are graphically represented as nodes in a tree The nodes are labeled with the syntactic category of the constituent Preferred illustration scheme in papers, textbooks.

19 Brackets v.s. tree (S ( (DT the) (ADJ little) (NN boy)) ( (VV fed) ( (DT the) (NN cat))) (PU.)) S DT JJ NNVV DT The little boy fed the NN cat. A constituent is exhaustively included in a pair of brackets. A constituent is exhaustively dominated by a node.

20 Representing discontinuous constituents Is there a way of making discontinuous constituents continuous (or alternatively, making long-distance dependencies local)? The answer: using trace! Believers and doubters of trace

21 Passivization -1 S Localized! NN AUX VV PP P *-1 NN Arthur was attacked by aliens

22 WH-question SBARQ SQ WHAD AUX Localized! VV AD DT NN Where did you put the book *T*-1

23 Relative clause WH-1 SBAR S localized DT VV NN promises that the president made *T*-1

24 Topicalization S -4 VV PP P NN This every man contains *T*-4 within them

25 Tough movement S VBP ADJP JJ SBAR WH-9 S TO VB IN PP Cars are tough 0 * to pay for *T*-9

26 Right-hand node raising S VBPAD AD SBAR SBAR CC SBAR PP WH S WH S VBP VBP PP PP One knows better now who *T*-1 has bone *RNR*-5 and who *T*-2 has jelly *RNR*-5 in his spine.

27 Interpret constituent here S SBAR VBD SBAR-9 WNHP S PP VBD PP A young woman *ICH*-9 entered whom she at once recognized *T*-2 as Jane Doe II.

28 Structural ambiguity Wanted: Man to take care of cow that does not smoke or drink. Question: how do we represent the interpretation that non-smoking and non-drinking man is sought to take care of cow?

29 Some examples Enraged cow injures farmer with ax Teacher Strikes Idle Kids Teller Stuns Man with Stolen Check

30 Teacher strikes idle kids S VBP NN JJ NNS Teacher strikes idle kids

31 Teacher strikes idle kids S VBP NN NNS NNS Teacher strikes idle kids

32 Enraged cow injures farmer with ax S VBP PP IN Enraged cow injures farmer with ax

33 Enraged cow injures farmer with ax S VBP IN PP Enraged cow injures farmer with ax

34 Teller Stuns Man with Stolen Check S VBP PP IN Teller Stuns Man with Stolen Check

35 Teller Stuns Man with Stolen Check S VBP PP IN Teller Stuns Man with Stolen Check

36 Categorizing ambiguity Part of speech Conjunction Subordinate clause: higher or lower verbs? Prepositional phrase: verb or noun?

37 Computer ambiguity v.s. human ambiguity S MD CC VB VBP PP The post office will hold out discounts and service concessions as incentives

38 Computer ambiguity v.s. human ambiguity S MD VB PP CC The post office will hold out discounts and service concessions as incentives

39 Computer ambiguity v.s. human ambiguity S MD VB CC NN NNS PP The post office will hold out discounts and service concessions as incentives

40 Computer ambiguity v.s. human ambiguity S MD VB IN PP CC PP The post office will hold out discounts and service concessions as incentives

41 Computer ambiguity v.s. human ambiguity S CC VB VBD PP The post office will hold out discounts and service concessions as incentives

42 Computer ambiguity vs human ambiguity Computer: I can t interpret this. There is so much ambiguity. Human: Ambiguity? What ambiguity? C: Well, first of all, will can be a noun or a modal verb H: No Silly, will can t be a noun here. C: how do I know that? H: You know because there is no such thing as the post office will hold. C: huh???

43 How many interpretations? The agency sees widespread use of the codes as a way of handling the rapidly growing mail volumes and controlling labor costs

44 Discussion question Should a sentence be annotated with multiple interpretations in a treebank?

45 Discussion question No, unless there is no way to resolve the ambiguity from the context provided in the text. Even then it may be a good idea to just choose one interpretation. Machines learn how ambiguities are resolved by examining a large amount of human annotated data and this approach is call supervised machine learning.

46 Discussion question Can all semantic dependencies be represented with attachment at the appropriate level?

47 How do we represent the dependency between the PP and the Noun? S AD PP P VV DT NN

48 Penn Treebank representation scheme Configurational representation with phrasal labels Non-configurational representation with functional tags Using empty categories to localize nonlocal dependencies

49 PTB Phrasal labels S: Simple declarative sentence SBAR: Clause introduced by a subordinate conjunction SBARQ: Direction question introduced by a wh-word or wh-phrase SINV: Inverted declarative sentence SQ: Inverted yes-no question ADJP: adjective phrase AD: adverbial phrase CONJP: conjunction phrase FRAG: fragmentary phrase INTJ: interjection phrase LST: list NAC: not a constituent

50 PTB Phrasal labels (cont d) : noun phrase NX: used within certain complex s to mark the head of the. PP: prepositional phrase PRN: parenthetical PRT: particle QP: quantifier phrase RRC: reduced relative clause UCP: unlike coordinated phrase : Verb phrase WHADJP: Wh-adjective phrase WHAD: Wh-adverb phrase WH: Wh-noun phrase WHPP: Wh-prepositional phrase X: unknown, uncertain, or unbracketable

51 A PTB example (S (-SBJ The Mortgage and equity real estate investment trust) (AD last) ( (VBD paid) ( a dividend)) (PP-TMP (IN on) ( August 1, 1988)))

52 CTB Phrasal labels ADJP: adjectival phrase AD: adverbial phrase CLP: classifier phrase, e.g., CP: complementizer phrase D: DP: determiner phrase, e.g., FRAG: fragmentary phrase IP: Inflectional phrase LCP: localizer phrase, e.g., LST: list, e.g., : noun phrase PP: prepositional phrase PRN: parenthetical QP: quantifier phrase UCP: unlike coordinate phrase : verb phrase

53 An improvement (?) made in the Chinese Treebank One grammatical relation per bracket

54 Complementation (left-headed) XP X YP {ZP} DP PP DT QP VV P

55 Complementation (right-headed) XP YP X CP D LCP IP DEC DEG /IP LC

57 Coordination XP XP CC XP CC PU CC

58 One grammatical relation per bracket

59 Levy and Manning (2003) Is it harder to parse Chinese or Chinese Treebank? Should theoretical linguistics and corpus linguistics use the same criteria to decide what counts as a good parse and a good representation scheme? Should the quality of a corpus be validated by a parser?

60 References Santorini,Beatrice and Anthony Kroch The syntax of natural language: An online introduction using the Trees program. Marcus, Mitchell P., Beatrice Santorini and Mary Ann Marcinkiewicz Building a large annotated corpus of English: the Penn Treebank. Computational linguistics, vol. 19 Xue, Nianwen, Fei Xia, Fu-dong Chiou and Martha Palmer Phrase structure annotation of a large Chinese corpus: the Chinese Treebank. Natural Language Engineering, 11(2)-207.

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