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CS60057 Speech &Natural Language Processing

CS60057 Speech &Natural Language Processing. Autumn 2007. Lecture 16 5 September 2007. Parsing with features. We need to constrain the rules in CFGs, for example to coerce agreement within and between constituents to pass features around to enforce subcategorisation constraints

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CS60057 Speech &Natural Language Processing

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  1. CS60057Speech &Natural Language Processing Autumn 2007 Lecture 16 5 September 2007 Natural Language Processing

  2. Parsing with features • We need to constrain the rules in CFGs, for example • to coerce agreement within and between constituents • to pass features around • to enforce subcategorisation constraints • Features can be easily added to our grammars • And later we’ll see that feature bundles can completely replace constituents Natural Language Processing

  3. Parsing with features • Rules can stipulate values, or placeholders (variables) for values • Features can be used within the rule, or passed up via the mother nodes • Example: subject-verb agreement S  NP VP [if NP and VP agree in number] number of NP depends on noun and/or determiner number of VP depends on verb S  NP(num=X) VP (num=X) NP (num=X)  det(num=X) n (num=X) VP(num=X)  v(num=X) NP(num=?) Natural Language Processing

  4. this man n(num=pl) men Declarative nature of features NP (num=X)  det(num=X) n (num=X) The rules can be used in various ways • To build an NP only if det and n agree (bottom-up) • When generating an NP, to choose an n which agrees with the det (if working L-to-R) (top-down) • To show that the num value for an NP comes from its components (percolation) • To ensure that the num value is correctly set when generating an NP (inheritance) • To block ill-formed input this det (num=sg) these det (num=pl) the det (num=?) man n (num=sg) men n (num=pl) NP (num=sg) det(num=sg) n(num=sg) Natural Language Processing

  5. the man Use of variables NP (num=X)  det(num=X) n (num=X) • Unbound (unassigned) variables (ie variables with a free value): • the can combine with any value for num • Unification means that the num value for the is set to sg this det (num=sg) these det (num=pl) the det (num=?) man n (num=sg) men n (num=pl) NP (num=sg) det(num=?) n(num=sg) Natural Language Processing

  6. Parsing with features • Features must be compatible • Formalism should allow features to remain unspecified • Feature mismatch can be used to block false analyses, and disambiguate • e.g. they can fish ~ he can fish ~ he cans fish • Formalism may have attribute-value pairs, or rely on argument position e.g. NP(_num,_sem)  det(_num) n (_num,_sem) an = det(sing) the = det(_num) man = n(sing,hum) Natural Language Processing

  7. Parsing with features VP  v e.g. dance VP v NP e.g. eat VP  v NP NP e.g. give VP  v PP e.g. wait (for) • Using features to impose subcategorization constraints VP(_num)  v(_num,intr) VP(_num)  v(_num,trans) NP VP(_num)  v(_num,ditrans) NP NP VP(_num)  v(_num,prepobj(_case)) PP(_case) PP(_case)  prep(_case) NP dance = v(plur,intr) dances = v(sing,intr) danced = v(_num,intr) waits = v(sing,prepobj(for)) for = prep(for) Natural Language Processing

  8. S NP VP (_num) (_num) v (sing,intrans) v NP (sing,trans) (_1) det n (_num) (_num) det n (_1) (_1) shot the man those elephants Parsing with features (top-down) S  NP(_num) VP(_num) NP(_num)  det(_num) n(_num) VP(_num)  v(_num,intrans) VP(_num)  v (_num,trans) NP(_1) the man shot those elephants S  NP(_num) VP(_num) NP(_num)  det(_num) n(_num) the = det(_num) man = n(sing) (sing) (sing) _num=sing VP(sing)  v(sing,intrans) shot = v(sing,trans) (sing) (pl) VP(sing)  v(sing,trans) NP(_1) (sing) shot = v(sing,trans) NP(_1)  det(_1) n(_1) (pl) (pl) those = det(pl) elephants = n(pl) Natural Language Processing

  9. ATTR1 VAL1 ATTR2 VAL2 ATTR3 VAL3 NUM SG PERS 3 CAT NP NUMBER SG PERSON 3 CAT NP AGR Feature structures • Instead of attaching features to the symbols, we can parse with symbols made up entirely of attribute-value pairs: “feature structures” • Can be used in the same way as seen previously • Values can be atomic … • … or embedded feature structures … Natural Language Processing

  10. UnificationProbabilistic CFG August 31, 2006 Natural Language Processing

  11. Feature Structures • A set of feature-value pairs • No feature occurs in more than one feature-value pair (a partial function from features to values) • Circular structures are prohibited. Natural Language Processing

  12. Structured Feature Structure Part of a third-person singular NP: Natural Language Processing

  13. Reentrant Feature Structure • Two features can share a feature structure as value. Not the same thing as them having equivalent values! • Two distinct feature structure values: • One shared value (reentrant feature structure): Natural Language Processing

  14. AGR 1 SUBJ [ AGR 1 ] NUM SG PERS 3 • they can be coindexed CAT S HEAD Natural Language Processing

  15. AGR 1 SUBJ [ AGR 1 ] NUM SG PERS 3 Parsing with feature structures • Grammar rules can specify assignments to or equations between feature structures • Expressed as “feature paths” e.g. HEAD.AGR.NUM = SG CAT S HEAD Natural Language Processing

  16. Subsumption () • (Partial) ordering of feature structures • Based on relative specificity • The second structure carry less information, but is more general (or subsumes) the first. Natural Language Processing

  17. Subsumption • A more abstract (less specific) feature structure subsumes an equally or more specific one. • A feature structure F subsumes a feature structure G ( F  G) if and only if : • For every structure x in F, F(x) G(x) (where F(x) means the value of the feature x of the feature structure F). • For all paths p and q in F such that F(p)=F(q), it is also the case that G(p)=G(q). • An atomic feature structure neither subsumes nor is subsumed by another atomic feature structure. • Variables subsume all other feature structures. • A feature structure F subsumes a feature structure G (F  G) if if all parts of F subsumes all parts of G. Natural Language Processing

  18. Subsumption Example Consider the following feature structures: (1) (2) (3) (1)  (3) (2)  (3) but there is no subsumption relation between (1) and (2) Natural Language Processing

  19. Feature Structures in The Grammar • We will incorporate the feature structures and the unification process as follows: • All constituents (non-terminals) will be associated with feature structures. • Sets of unification constraints will be associated with grammar rules, and these rules must be satisfied for the rule to be satisfied. • These attachments accomplish the following goals: • To associate feature structures with both lexical items and instances of grammatical categories. • To guide the composition of feature structures for larger grammatical constituents based on the feature structures of their component parts. • To enforce compatibility constraints between specified parts of grammatical constraints. Natural Language Processing

  20. CAT NP NUMBER ?? PERSON 3 NUMBER SG PERSON 3 CAT NP NUMBER SG PERSON 3 Feature unification • Feature structures can be unified if • They have like-named attributes that have the same value: [NUM SG]  [NUM SG] = [NUM SG] • Like-named attributes that are “open” get the value assigned:  = Natural Language Processing

  21. NUM SG PERS 3 CAT NP NUMBER SG CAT NP NUMBER SG PERSON 3 CAT NP AGR Feature unification • Complementary features are brought together • Unification is recursive • Coindexed structures are identical (not just copies): assignment to one effects all  = [PERSON 3] CAT NP AGR [NUM SG] CAT NP AGR [PERS 3]  = Natural Language Processing

  22. VAL INDEF NUM SG Example CAT N AGR _2 SEM _3 CAT NP AGR _1  _2 SEM _3 CAT DET AGR _1  CAT DET AGR  a CAT DET AGR [VAL DEF]  the CAT N LEX “man” AGR [NUM SG] SEM HUM  man Natural Language Processing

  23. CAT DET AGR [VAL DEF]  the CAT N LEX “man” AGR [NUM SG] SEM HUM  man the man the the man CAT N AGR _2 SEM _3 CAT NP AGR _1  _2 SEM [_3] CAT DET AGR _1  LEX “man” AGR [NUM SG] SEM HUM VAL DEF [VAL DEF] NUM SG HUM Natural Language Processing

  24. CAT DET AGR  a VAL INDEF AGR NUM SG VAL INDEF NUM SG VAL INDEF NUM SG VAL INDEF NUM SG CAT N LEX “man” AGR [NUM SG] SEM HUM  man a a man a man CAT NP AGR _1  _2 SEM [_3] CAT N AGR _2 SEM _3 CAT DET AGR _1  LEX “man” AGR [NUM SG] SEM HUM [NUM SG] HUM Natural Language Processing

  25. Types and inheritance • Feature typing allows us to constrain possible values a feature can have • e.g. num = {sing,plur} • Allows grammars to be checked for consistency, and can make parsing easier • We can express general “feature co-occurrence conditions” … • And “feature inheritance rules” • Both these allow us to make the grammar more compact Natural Language Processing

  26. Co-occurrence conditions and Inheritance rules • General rules (beyond simple unification) which apply automatically, and so do not need to be stated (and repeated) in each rule or lexical entry • Examples: [cat=np]  [num=??, gen=??, case=??] [cat=v,num=sg]  [tns=pres] [attr1=val1]   [attr2=val2] Natural Language Processing

  27. Inheritance rules • Inheritance rules can be over-ridden e.g. [cat=n]  [gen=??,sem=??] sex={male,female} gen={masc,fem,neut} [cat=n,gen=fem,sem=hum]  [sex=female] uxor [cat=n,gen=fem,sem=hum] agricola [cat=n,gen=fem,sem=hum,sex=male] Natural Language Processing

  28. Unification in Linguistics • Lexical Functional Grammar • If interested, see PARGRAM project • GPSG, HPSG • Construction Grammar • Categorial Grammar Natural Language Processing

  29. Unification • Joining the contents of two feature structures into one new (the union of the two originals). • The union is most general feature structure subsumed by both. • The union of two contradictory feature structures is undefined (unification fails). Natural Language Processing

  30. Unification Constraints • Each grammar rule will be associated with a set of unification constraints. 0  1 … n {set of unification constraints} • Each unification constraint will be in one of the following forms. < i feature path> = Atomic value < i feature path> = < j feature path> Natural Language Processing

  31. Unification Constraints -- Example • For example, the following rule S  NP VP Only if the number of the NP is equal to the number of the VP. will be represented as follows: S  NP VP <NP NUMBER> = <VP NUMBER> Natural Language Processing

  32. Agreement Constraints S  NP VP <NP NUMBER> = <VP NUMBER> S  Aux NP VP <Aux AGREEMENT> = <NP AGREEMENT> NP  Det NOMINAL <Det AGREEMENT> = <NOMINAL AGREEMENT> <NP AGREEMENT> = <NOMINAL AGREEMENT> NOMINAL  Noun <NOMINAL AGREEMENT> = <Noun AGREEMENT> VP  Verb NP <VP AGREEMENT> = <Verb AGREEMENT> Natural Language Processing

  33. Agreement Constraints -- Lexicon Entries Aux  does <Aux AGREEMENT NUMBER> = SG <Aux AGREEMENT PERSON> = 3 Aux  do <Aux AGREEMENT NUMBER> = PL Det  these <Det AGREEMENT NUMBER> = PL Det  this <Det AGREEMENT NUMBER> = SG Verb  serves <Verb AGREEMENT NUMBER> = SG <Verb AGREEMENT PERSON> = 3 Verb  serve <Verb AGREEMENT NUMBER> = PL Noun  flights <Noun AGREEMENT NUMBER> = PL Noun  flight <Noun AGREEMENT NUMBER> = SG Natural Language Processing

  34. Head Features • Certain features are copied from children to parent in feature structures. Example: • AGREEMENT feature in NOMINAL is copied into NP. • The features for most grammatical categories are copied from one of the children to the parent. • The child that provides the features is called head of the phrase, and the features copied are referred to as head features. • A verb is a head of a verb phrase, and a nominal is a head of a noun phrase. We may reflect these constructs in feature structures as follows: NP  Det NOMINAL <Det HEAD AGREEMENT> = <NOMINAL HEAD AGREEMENT> <NP HEAD> = <NOMINAL HEAD> VP  Verb NP <VP HEAD> = <Verb HEAD> Natural Language Processing

  35. SubCategorization Constraints • For verb phrases, we can represent subcategorization constraints using three techniques: • Atomic Subcat Symbols • Encoding Subcat lists as feature structures • Minimal Rule Approach (using lists directly) • We may use any of these representations. Natural Language Processing

  36. Atomic Subcat Symbols VP  Verb <VP HEAD> = <Verb HEAD> <VP HEAD SUBCAT> = INTRANS VP  Verb NP <VP HEAD> = <Verb HEAD> <VP HEAD SUBCAT> = TRANS VP  Verb NP NP <VP HEAD> = <Verb HEAD> <VP HEAD SUBCAT> = DITRANS Verb  slept <Verb HEAD SUBCAT> = INTRANS Verb  served <Verb HEAD SUBCAT> = TRANS Verb  gave <Verb HEAD SUBCAT> = DITRANS Natural Language Processing

  37. Encoding Subcat Lists as Features Verb  gave <Verb HEAD SUBCAT FIRST CAT> = NP <Verb HEAD SUBCAT SECOND CAT> = NP <Verb HEAD SUBCAT THIRD> = END VP  Verb NP NP <VP HEAD> = <Verb HEAD> <VP HEAD SUBCAT FIRST CAT> = <NP CAT> <VP HEAD SUBCAT SECOND CAT> = <NP CAT> <VP HEAD SUBCAT THIRD> = END • We are only encoding lists using positional features Natural Language Processing

  38. Minimal Rule Approach • In fact, we do not use symbols like SECOND, THIRD. They are just used to encode lists. We can use lists directly (similar to LISP). <SUBCAT FIRST CAT> = NP <SUBCAT REST FIRST CAT> = NP <SUBCAT REST REST> = END Natural Language Processing

  39. Subcategorization Frames for Lexical Entries • We can use two different notations to represent subcategorization frames for lexical entries (verbs). Verb  want <Verb HEAD SUBCAT FIRST CAT> = NP Verb  want <Verb HEAD SUBCAT FIRST CAT> = VP <Verb HEAD SUBCAT FIRST FORM> = INFINITITIVE Natural Language Processing

  40. Implementing Unification • The representation we have used cannot facilitate the destructive merger aspect of unification algorithm. • For this reason, we add additional features (additional edges to DAGs) into our feature structures. • Each feature structure will consists of two fields: • Content Field -- This field can be NULL or may contain ordinary feature structure. • Pointer Field -- This field can be NULL or may contain a pointer into another feature structure. • If the pointer field of a DAG is NULL, the content field of DAG contains the actual feature structure to be processed. • If the pointer field of a DAG is not NULL, the destination of that pointer represents the actual feature structure to be processed. Natural Language Processing

  41. Extended Feature Structures  Natural Language Processing

  42. SG C Num    P C Null    3 Per C P    Null P Null Extended DAG Natural Language Processing

  43. 3 SG C C     Num     P P C Null Null     Null Per P C P Null Unification of Extended DAGs Natural Language Processing

  44. 3 SG Per C C  Null     C  Num     P P C Null Null P  Null     P Null Per P C P Null Unification of Extended DAGs (cont.) Natural Language Processing

  45. Unification Algorithm functionUNIFY(f1,f2)returns fstructure or failure f1real real contents of f1 /* dereference f1 */ f2real real contents of f2 /* dereference f2 */ if f1real is Null then { f1.pointer  f2; returnf2; } else iff2real is Null then { f2.pointer  f1; returnf1; } else if f1real and f2real are identical then { f1.pointer  f2; returnf2; } else if f1real and f2real are complex feature structures then { f2.pointer  f1; for eachfeaturein f2realdo { otherfeature  Find or create a feature corresponding to feature in f1real; if UNIFY(feature.value,otherfeature.value) returns failure then return failure; } return f1; } elsereturn failure; Natural Language Processing

  46. Example - Unification of Complex Structures Natural Language Processing

  47. C C Agr C Sub C Num C Agr Agr C C C C Per • • • • • • • • • • • • • • • • SG • Null C Per • • • • • • • • • • Sub Null Null Null Null Null Null Null Null Null • Null • 3 Example - Unification of Complex Structures (cont.) Natural Language Processing

  48. Parsing with Unification Constraints • Let us assume that we have augmented our grammar with sets of unification constraints. • What changes do we need to make a parser to make use of them? • Building feature structures and associate them with sub-trees. • Unifying feature structures when sub-trees are created. • Blocking ill-formed constituents Natural Language Processing

  49. Earley Parsing with Unification Constraints • What do we have to do to integrate unification constraints with Early Parser? • Building feature structures (represented as DAGs) and associate them with states in the chart. • Unifying feature structures as states are advanced in the chart. • Blocking ill-formed states from entering the chart. • The main change will be in COMPLETER function of Earley Parser. This routine will invoke the unifier to unify two feature structures. Natural Language Processing

  50. Building Feature Structures NP  Det NOMINAL <Det HEAD AGREEMENT> = <NOMINAL HEAD AGREEMENT> <NP HEAD> = <NOMINAL HEAD> corresponds to Natural Language Processing

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