1. Computational lexicology, morphology and syntax Diana Trandabă 2015-2016

2. Dependency parsing The syntactic parsing of a sentence consists of finding the correct syntactic structure of that sentence in a given formalism/grammar. Dependency Grammar (DG) and Phrase Structure Grammar (PSG) are two such formalisms.

3. Phrase Structure Grammar (PSG) Breaks sentence into constituents (phrases) – Which are then broken into smaller constituents Describes phrase structure, clause structure –E.g.. NP, PP, VP etc.. Structures often recursive –The clever tall blue-eyed old … man

4. Constituents & Constituency Tests Constituents = the natural groupings of a sentence A constituent is a string of words such that there is one node that dominates those words and no other words.

5. Sam climbed up the ladder. Sam picked up the ladder. N V P Det N NP VP S V NP VP PP S Tree 1 Tree 2

6. Discussion What are the constituents of Tree 1 and Tree 2? Which strings of words are constituents in one tree but not the other?

7. Well-formed constituents –A constituent is well formed if… –1) a group of words can stand alone Ex. “What did you find?” “A puppy” (not “found a”) –2) pronouns can substitute for natural groups Ex. “Where did you find a puppy?” “I found HIM in the park.” –3) a group of words can be move. [move unit] Ex. It was [a puppy] that the child found. [A puppy] was found by the child.

8. Sentences: Hierarchical Organization The boy raced the girl. Words grouped into natural units: [ The boy ] [ raced the girl ]. Further division: [ [ The ] [ boy ] ] [ [ raced ] [ [ the ] [ girl ] ] ]. Tree Diagram: The boy raced thegirl noun phrase verb phrase noun phrase

9. Exercise Goals of the exercise: –Relying on tests when your intuition fails –Adapting to inconsistent results (e.g., find evidence for disqualifying some of the tests) The five trees on the following slide have all been proposed by linguists, in published articles, for the sentence: He has been writing a letter.

10. TREE 1 He has been writing a letter. NP PERF PROG V NP AUX VP S TREE 2 He has been writing a letter. NP V NP V VP S VP TREE 3 He has been writing a letter. NP V NP V VP VP S AUX TREE 4 NP PERF PROG V NP AUX V VP S He has been writing a letter. NP V V NP AUX VP S VP TREE 5

11. [reminder] Phrase Structure Tree

12. Dependency Grammar  Syntactic structure consists of lexical items, linked by binary asymmetric relations called dependencies  Interested in grammatical relations between individual words (governing & dependent words)  Does not propose a recursive structure  Rather a network of relations  These relations can also have labels

13. Dependency Tree Example Phrasal nodes are missing in the dependency structure when compared to constituency structure.

14. Dependency Tree with Labels

24. Comparison Dependency structures explicitly represent –Head-dependent relations (directed arcs) –Functional categories (arc labels) –Possibly some structural categories (parts-of-speech) Phrase structure explicitly represent –Phrases (non-terminal nodes) –Structural categories (non-terminal labels) –Possibly some functional categories (grammatical functions)

25. Comparison Dependency Parsing is more straightforward –Parsing can be reduced to labeling each token pair w i and w j Direct encoding of predicate-argument structure –Fragments are directly interpretable Dependency structure independent of word order –Suitable for free word order languages (like Indian languages)

26. Predicate-argument structure Predicates (predicational words) designate events, properties of, or relations between, entities. Linguistic expressions can be dependent or independent. –hat - can be understood outside any circumstance, time, or person, it is an individual. –red - cannot be understood outside its association with an individual: red hat. In linguistic terms, dependent phenomena are predicates, while individuals are arguments. Predication - the way individuals instantiate properties, actions, attributes and states.

27. Predicate-argument structure Who, what, where, when, why? – Predicates Verbs: sell, buy, cost, etc. Nouns: acquisition, etc. – Arguments buyer seller goods money time etc..

28. Examples of types of Arguments (Semantic Roles) Agent: [Bill] Agent ate his soup quietly. Experiencer: The smell of lilies filled [Jennifer's] Experiencer nostrils. Theme: I like [Kim] Theme. Patient: The falling rocks crushed [the car] Patient. Instrument: Jamie cut the ribbon [with a pair of scissors] Instrument. Location: Johnny and Linda played carelessly [in the park] Location. Recipient: I sent [John] Recipient the letter. Source: The rocket was launched [from Central Command] Source. Time: The rocket was launched [yesterday] Time. Beneficiary: I baked [Reggie] Beneficiary a cake. Manner: [With great urgency] Manner, Agatha phoned 911. Purpose: Agatha phoned 911 right away [in order to get some help]P urpose. Cause: [Since Clyde was hungry] Cause, he ate the cake.

29. Valence A predicational word needs, in order to complete its sense, arguments (mandatory) and adjuncts (optional). Arguments: –John reads a book. (valence 2) –John gives Mary a book. (valence 3) Adjuncts (circumstantial complements) –John reads a book in the train. –John gave Mary a book for her birthday. Arguments can be established for verbs, nouns or adjectives: –John presented the situation. –John's presentation of the situation was very clear. –The presented situation was simple.

30. Dependency Tree Formal definition –An input word sequence w 1 …w n –Dependency graph D = (W, E) where W is the set of nodes i.e. word tokens in the input seq. E is the set of unlabeled tree edges (w i, w j ) (w i, w j є W). (w i, w j ) indicates an edge from w i (parent) to w j (child). Task of mapping an input string to a dependency graph satisfying certain conditions is called dependency parsing

31. Well-formedness A dependency graph is well-formed iff –Single head: Each word has only one head. –Acyclic: The graph should be acyclic. –Connected: The graph should be a single tree with all the words in the sentence. –Projective: If word A depends on word B, then all words between A and B are also subordinate to B (i.e. dominated by B).

32. Non-projective dependency tree Ram saw a dog yesterday which was a Yorkshire Terrier * Crossing lines English has very few non-projective cases.

33. Dependency Parsing Dependency based parsers can be broadly categorized into –Grammar driven approaches Parsing done using grammars. –Data driven approaches Parsing by training on annotated/un-annotated data. These approaches are not mutually exclusive

34. Parsing Methods Three main traditions –Dynamic programming CKY, Eisner, McDonald –Constraint satisfaction Maruyama, Foth et al., Duchier –Deterministic search Covington, Yamada and Matsumuto, Nivre

35. Dynamic Programming Basic Idea: Treat dependencies as constituents. Use, e.g., CKY parser (with minor modifications)

36. Dependency Chart Parsing Grammar is regarded as context-free, in which each node is lexicalized Chart entries are subtrees, i.e., words with all their left and right dependents Problem: Different entries for different subtrees spanning a sequence of words with different heads Time requirement: O(n 5 )

37. Dynamic Programming Approaches Original version [Hays 1964] ( grammar driven ) Link grammar [Sleator and Temperley 1991] ( grammar driven ) Bilexical grammar [Eisner 1996] ( data driven ) Maximum spanning tree [McDonald 2006] ( data driven )

38. Constraint Satisfaction Uses Constraint Dependency Grammar Grammar consists of a set of boolean constraints, i.e. logical formulas that describe well-formed trees A constraint is a logical formula with variables that range over a set of predefined values Parsing is defined as a constraint satisfaction problem Constraint satisfaction removes values that contradict constraints

39. Constraint Satisfaction Parsing is an eliminative process rather than a constructive one such as in CFG parsing Constraint satisfaction in general is NP complete Parser design must ensure practical efficiency Different approaches –Constraint propagation techniques which ensure local consistency [Maruyama 1990] –Weighted CDG [Foth et al. 2000, Menzel and Schroder 1998]

40. Weighted Constraint Parsing Robust parser, which uses soft constraints Each constraint is assigned a weight between 0.0 and 1.0 Weight 0.0: hard constraint, can only be violated when no other parse is possible Constraints assigned manually (or estimated from treebank) Efficiency: uses a heuristic transformation-based constraint resolution method

41. Transformation-Based Constraint Resolution Heuristic search Very efficient Idea: first construct arbitrary dependency structure, then try to correct errors Error correction by transformations Selection of transformations based on constraints that cause conflicts Anytime property: parser maintains a complete analysis at anytime → can be stopped at any time and return a complete analysis

42. Deterministic Parsing Basic idea: –Derive a single syntactic representation (dependency graph) through a deterministic sequence of elementary parsing actions – Sometimes combined with backtracking or repair Motivation: –Psycholinguistic modeling –Efficiency –Simplicity

43. Shift-Reduce Type Algorithms Data structures: –Stack [...,w i ]S of partially processed tokens –Queue [w j,...]Q of remaining input tokens Parsing actions built from atomic actions: –Adding arcs (w i → w j, w i ← w j ) –Stack and queue operations Left-to-right parsing Restricted to projective dependency graphs

44. Yamada and Matsumoto Parsing in several rounds: deterministic bottom-up O(n 2 ) Looks at pairs of words 3 actions: shift, left, right Shift: shifts focus to next word pair

45. Yamada and Matsumoto Right: decides that the right word depends on the left word  Left: decides that the left word depends on the right one

46. Parsing Algorithm Go through each pair of words –Decide which action to take If a relation was detected in a pass, do another pass E.g. the little girl –First pass: relation between little and girl –Second pass: relation between the and girl Decision on action depends on word pair and context

47. Classifier-Based Parsing Data-driven deterministic parsing: –Deterministic parsing requires an oracle. –An oracle can be approximated by a classifier. –A classifier can be trained using treebank data. Learning algorithms: –Support vector machines (SVM) [Kudo and Matsumoto 2002, Yamada and Matsumoto 2003,Isozaki et al. 2004, Cheng et al. 2004, Nivre et al. 2006] –Memory-based learning (MBL) [Nivre et al. 2004, Nivre and Scholz 2004] –Maximum entropy modeling (MaxEnt ) [Cheng et al. 2005]

48. Feature Models Learning problem: –Approximate a function from parser states, represented by feature vectors to parser actions, given a training set of gold standard derivations. Typical features: –Tokens and POS tags of : Target words Linear context (neighbors in S and Q) Structural context (parents, children, siblings in G) –Can not be used in dynamic programming algorithms.

49. Dependency Parsers for download MST parser by Ryan McDonald Malt parser by Joakim Nivre Stanford parser

50. Great! See you next time!