2. 74.793 NLP and Speech 2004 Feature Structures Feature Structures and Unification

3. Feature Structures - General Feature structures describe linguistic attributes or features like number, person associated with words or syntactic constituents like noun phrase. Feature structures are sets of features and values, e.g. hat[Numbersing ] buys[Person 3 ] [Numbersing ]

4. Feature Structures - Agreement Feature structures can be collected in one ‘variable’ called agreement. buys agreement [Person 3] [Number sing]

5. Feature Structures, Grammar, Parsing Feature Structures describe additional syntactic-semantic information, like category, person, number, e.g. goes  specify feature structure constraints (agreements) as part of the grammar rules during parsing, check agreements of feature structures (unification) example S → NP VP =

6. Feature Structures as Constraints Ungrammatical sentences like “He go” or “We goes” can be excluded using feature constraints. example S → NP VP = =

7. Add to feature structure category cat: buys cat verb agreement [Person 3 ] [Number sing] Feature Structures and Categories

8. Compare and combine feature structures: he buys buys cat verb agreement [Person 3 ] [Number sing] he cat noun agreement [Person 3 ] [Number sing] Feature Structures and Unification 1

9. S → NP VP = = buys cat verb agreement [Person 3 ] [Number sing] he cat noun agreement [Person 3 ] [Number sing] Using Feature Structures

10. Unification of Feature Structures Agreement is checked by the unification operation according to the following rules: [feature i value i ] |_| [feature i value i ] = [feature i value i ] [feature i value i ] |_| [feature i value j ] = fail if value i  value j [feature i value i ] |_| [feature i undef.] = [feature i value i ] [feature i value i ] |_| [feature j value j ] = feature i value i feature j value j if feature i  feature j

11. Features and Subcategorization 1 NP modifiers or Verb complements central noun + modifiers + agreement central verb + complements + agreements “... the man who chased the cat out of the house...” “... the man chased the barking dog who bit him...” Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP: determined by

12. Features and Subcategorization 2 NP modifiers or Verb complements: central noun + modifiers + agreement central verb + complements + agreements “... the man who chased the cat out of the house...” “... the man chased the barking dog who bit him...” Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP: determined by

13. Semantics

14. Distinguish between surface structure (syntactic structure) and deep structure (semantic structure) of sentences. Different forms of Semantic Representation logic formalisms ontology / semantic representation languages –Case Frame Structures (Filmore) –Conceptual Dependy Theory (Schank) –DL and similar KR languages –Ontologies

15. Semantic Representations Semantic Representations based on some form of (formal) Representation Language. –Semantics Networks –Conceptual Dependency Graphs –Case Frames –Ontologies –DL and similar KR languages

16. Constructing a Semantic Representation General:  Start with surface structure  Derived from parser.  Map surface structure to semantic structure  Use phrases as sub-structures.  Find concepts and representations for central phrases (e.g. VP, NP, then PP)  Assign phrases to appropriate roles around central concepts (e.g. bind PP into VP representation).

17. Ontology (Interlingua) approach Ontology: a language-independent classification of objects, events, relations A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology An analyzer that constructs Interlingua representations and selects (an?) appropriate one (based on Steve Helmreich's 419 Class, Nov 2003)

18. Semantic Lexicon Provides a syntactic context for the appearance of the lexical item Provides a mapping for the lexical item to a node in the ontology (or more complex associations) Provides connections from the syntactic context to semantic roles and constraints on these roles

19. Deriving Basic Semantic Dependency (a toy example) Input: John makes tools Syntactic Analysis: catverb tensepresent subject root john catnoun-proper object root tool catnoun numberplural Deriving Basic Semantic Dependency

20. John-n1 syn-struc rootjohn catnoun-proper sem-struc human name john gendermale tool-n1 syn-struc roottool catn sem-struc tool Lexicon Entries for John and tool

21. Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make: manufacturing-activity... agenthuman themeartifact … Meaning Representation - Example make

22. John-n1 syn-struc rootjohn catnoun-proper sem-struc human name john gendermale tool-n1 syn-struc roottool catn sem-struc tool Relevant parts of the (appropriate senses of the) lexicon entries for John and tool

23. The basic semantic dependency component of the TMR for John makes tools manufacturing-activity-7 agentuman-3 themeset-1 element tool cardinality> 1 … Semantic Dependency Component

24. try-v3 syn-struc root try cat v subj root $var1 cat n xcomp root $var2 cat v formOR infinitive gerund sem-struc set-1element-typerefsem-1 cardinality>=1 refsem-1 semevent agent^$var1 effectrefsem-2 modality modality-typeepiteuctic modality-scoperefsem-2 modality-value< 1 refsem-2value^$var2 semevent

25. Constructing an IL representation For each syntactic analysis: Access all semantic mappings and contexts for each lexical item. Create all possible semantic representations. Test them for coherency of structure and content.

26. “Why is Iraq developing weapons of mass destruction?”

27. Word sense disambiguation  Constraint checking – making sure the constraints imposed on context are met  Graph traversal – is-a links are inexpensive  Other links are more expensive  The “cheapest” structure is the most coherent  Hunter-gatherer processing

28. Logic Formalisms Lambda Calculus

29. Semantics - Lambda Calculus 1 Logic representations often involve Lambda-Calculus: represent central phrases (e.g. verb) as - expressions -expression is like a function which can be applied to terms insert semantic representation of complement or modifier phrases etc. in place of variables  x, y: loves (x, y)FOPLsentence x y loves (x, y) -expression, function x y loves (x, y) (John)  y loves (John, y)

30. Semantics - Lambda Calculus 2 Transform sentence into lambda-expression: “AI Caramba is close to ICSI.” specific: close-to (AI Caramba, ICSI) general:  x,y: close-to (x, y)  x=AI Caramba  y=ICSI Lambda Conversion: -expr: x y: close-to (x, y) (AI Caramba) Lambda Reduction: y: close-to (AI Caramba, y) close-to (AI Caramba, ICSI)

31. Semantics - Lambda Calculus 3 Lambda Expressions can be constructed from central expression, inserting semantic representations for complement phrases Verb  serves { x y  e IS-A(e, Serving)  Server(e,y)  Served(e,x)} represents general semantics for the verb 'serve Fill in appropriate expressions for x, y, for example 'meat' for y derived from Noun in NP as complement to Verb.

32. References Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, 2000. (Chapters 9 and 10) Helmreich, S., From Syntax to Semantics, Presentation in the 74.419 Course, November 2003.