1. DS-to-PS conversion Fei Xia University of Washington July 29, 2011 1

2. Main steps in building the treebank DS treebank: – Tokenization – Morphological analysis, voice, etc. – POS tagging – DS Propbank: adding Predicate-argument info Automatic DS-to-PS conversion Some manual check to ensure the conversion works well 2

3. Outline Important concepts Compatibility and consistency Handling inconsistency 3

4. Important concepts Linguistic phenomena Representation type Linguistic theory – Theoretical framework – Linguistic analyses Annotation guidelines 4

5. Linguistic phenomena They are what we want to present, including – General concepts: e.g., which words form a phrase? What types of phrases does a language have? – Types of relations between words or phrases (e.g., subjecthood, temporal modification) – Specific constructions (e.g., small clause) – Finer-grained distinctions (e.g., unergative vs. unaccusative) 5

6. Representation type It is the type of mathematical object that is used to represent syntactic facts Examples: DS, PS Each representation type can decide what more specific representation devices to employ – Labels on the arcs of a tree – Use of empty nodes or coindexation between nodes 6

7. Linguistic theory It explains how linguistic phenomena are represented in the chose representation type It has two components: – Theoretical framework: it provides vocabulary and constraints in which linguistic theories can be formulated: e.g., GB, LFG, LTAG, HPSG – Linguistic analyses 7

8. Small clause 8

9. “Exceptional case-marking” analysis 9

10. “Raising-to-object” analysis 10

11. Annotation guidelines Guideline designers need to choose the following – Linguistic phenomena to represent – Representation type – Theoretical framework – Linguistic analyses – Descriptions – Examples: sentences with DS or PS trees 11

12. Outline Important concepts Compatibility and consistency Handling inconsistency 12

13. “Exceptional case-marking” analysis 13

14. “Raising-to-object” analysis 14

15. Implicit vs. explicit information Certain aspects of information has to be expressed explicitly in DS, but not PS, or vice versa – Head in DS – Syntactic categories of phrases in PS Not explicitly providing info does not mean that corresponding concepts does not exist in DS/PS 15

16. Syntactic consistency We assume each phrase in a PS has a special word, head word, which represents the property of the phrase. A (DS, PS) pair is called consistent if there is a way to assign a head word to each internal node in the PS so that the resulting DS is identical to the given DS. 16

17. Consistent pairs 17

18. Inconsistent pairs 18

19. A real example 19

20. Consistency assumption 20

21. Definition of consistency A DS and a PS are consistent iff there exists a flattened version of the PS that is identical to the DS. If the input DS and the desired PS are consistent, the PS can be created by stretching the DS and adding syntactic labels. 21

22. Checking consistency For each (dep, head) pair in the DS – find their location in the PS and their closest antecedent – add heads to the nodes on the path between the leaf nodes and the antecedent The DS and the PS are consistent iff each node in the PS has exactly one head. 22

23. (Vinken, join) (Vinken) (join) (board, join) (board) (will, join) (29, join) (29) 23

24. Outline Important concepts Compatibility and consistency Handling inconsistency 24

25. wh-movement (who, come) come (come, think) 25

26. wh-movement (who, come) (come, think) come come | think come 26

27. wh-movement (who, come) (come, think) come ?? think ?? (you, think) 27

28. Can DS and PS be inconsistent? DS and PS can represent different aspects of the same overall pictures, and still be consistent. – Info provided in PropBank: e.g., empty subject, unaccusative – Info that is in PS only: e.g., traces DS and PS should not choose “conflicting” analyses. – DS and PS are two images of the same underlying treebank, not two separate treebanks. – Ex: ba-construction in Chinese: verb, prep, or something else? – Ex: free relatives: empty nominal head The inconsistency cases should be rare and well-motivated. 28

29. How to handle inconsistency? Detect inconsistency in (DS, PS) pairs in the guidelines Consult guideline designers to determine whether the inconsistency can be resolved by changing analyses If not, introduce DS cons and ensure sufficient info is in DS for automatic conversion. 29

30. Two-stage conversion DS to DS cons : by removing “inconsistency” between DS and PS. DS cons to PS: by applying conversion rules 30

31. Case #1: long-distance movement 31 DS const: DS prop: Other examples: extraposition Easily detectable due to non-projectivity Create DS const by moving up the “moved element” and leaving a trace which node is the “moved element”? The one that is apart from other nodes in the subtree.

32. Case #2: local scrambling 32 Detectable by assuming canonical word order: k1 > k2 Need from PS/DS teams the canonical word order and what word order triggers movement

33. Case #3: small clause rule 33 Detectable by dependency type k2s Need confirmation from IIIT that k2s is used only for small clause

34. Case 4: support verb 34 Detectable by dependency type “pof” Need confirmation from IIIT that “pof” is used only for support verb

35. Conclusion We define consistency between DS and PS DS and PS can be inconsistent but such cases should be rare and well-motivated. We will handle inconsistency with the two- stage approach 35

36. Conversion algorithm 36

37. Definition of conversion rule A conversion rule is a (DS_pattern, PS_pattern) pair. Ex: Simplest case: – DS_pattern corresponds to only one dependency link – Decomposing DS becomes trivial – PS_pattern is a tree fragment (e.g., wh-movement) – Learning rules from (PS, DS) pairs is easy 37

38. Extracting rules 38

39. Rules extracted from the example 39

40. Input DS 40

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42. Gluing PS segments together 42

43. c c c 43

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