74.793 NLP and Speech 2004 Semantics I Semantics II General Introduction Types of Semantics From Syntax to Semantics Semantics II Desiderata for Representation Logic-based Semantics

Semantics I

Semantics 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

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

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).

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)

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

Deriving Basic Semantic Dependency Deriving Basic Semantic Dependency (a toy example) Input: John makes tools Syntactic Analysis: cat verb tense present subject   root john cat noun-proper object   root     tool cat noun number plural

Lexicon Entries for John and tool John-n1 syn-struc root john cat noun-proper sem-struc human name john gender male tool-n1 syn-struc root tool cat n sem-struc tool

Meaning Representation - Example make Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make: manufacturing-activity ... agent human theme artifact …

Relevant parts of the (appropriate senses of the) lexicon entries for John and tool John-n1 syn-struc root john cat noun-proper sem-struc human name john gender male tool-n1 syn-struc root tool cat n sem-struc tool

Semantic Dependency Component The basic semantic dependency component of the TMR for John makes tools manufacturing-activity-7 agent uman-3 theme set-1 element tool cardinality > 1 …

try-v3 syn-struc root try cat v subj root $var1 cat n xcomp root $var2 form OR infinitive gerund sem-struc set-1 element-type refsem-1 cardinality >=1 refsem-1 sem event agent ^$var1 effect refsem-2 modality modality-type epiteuctic modality-scope refsem-2 modality-value < 1 refsem-2 value ^$var2 sem event

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.

“Why is Iraq developing weapons of mass destruction?”

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

Semantics II

Representation of Meaning Representation of meaning for natural language sentences: Semantic Representation Language (in most cases) = some kind of formal language + semantic primitives For example: First Order Predicate Logic with specific set of predicates and functions

Semantic Representations Semantic Representation based on some form of (formal) Representation Language. Semantics Networks Conceptual Dependency Graphs Case Frames Ontologies DL and similar KR languages First-Order Predicate Logic

Semantics - Connecting Words and Worlds Semantic Representation NL Input Knowledge Representation NL Output World State (KB: T-Box, A-Box)

Desiderata for a Semantic Representation Verifiability – semantic representation must be compatible with knowledge (base) of the system. Canonical Form - assign same representation to different surface expressions which have essentially the same meaning Ambiguity and Vagueness – representation should (in relation to knowledge base or information system access etc.) be unambiguous and precise

Example - NL Database Access Imagine a database access using natural language, i.e. questions to the DB posed in natural language. Example: DB of courses in the CS department Pose questions like: Who is teaching Advanced AI in Fall 2004? Is John Anderson teaching this term? What is John Anderson teaching this term? Who is teaching AI at the University of Winnipeg? Who is teaching an AI related course this term?

Example Story: My car was stolen two weeks ago. They found it last week. direct representation of meaning knowledge inference

Example car (my_car) stolen (my_car, t1), found (police, my-car, t2) t1<t2 ------------------------------------------------------------------- stolen (x, t1) and found (police, x, t2) implies has (owner (x), x, t3) with t3>t2 What can you infer if you instantiate x with my_car?

Example stolen (x, t1) and found (police, x, t2) implies has (owner (x), x, t3) with t3>t2 Express that if something is stolen, the owner does not have it!

Predicate-Argument Structure Verb-centered approach Thematic roles, case roles Describe semantic structure based on verb and associated roles filled by other parts of the sentence (phrases). Representation using e.g. logic: Transform structured input sentence (syntax!) into expression in predicate logic. Usually based on central predicate, the verb, or equivalent, like ‘be’+ adjective etc. Other parts of the sentence directly related to the verb go into the central predicate.

Verb Subcategorization Consider possible subcat frames of verbs. Example: 3 different kinds of want: NP want NP I want money. want1(Speaker, money) or want1(I, money) NP want Inf-VP He wants to go home. want2(he, go home) NP want NP Inf-VP I want him to go away. want3(I, him, go_away)

Example - Restaurant 'Maharani' Maharani serves vegetarian food. Maharani is a vegetarian restaurant. Maharani is close to ICSI. Write down logical formulas representing the three different sentences.

Logic Formalisms Lambda Calculus

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) FOPL sentence xy loves (x, y) -expression, function xy loves (x, y) (John)  y loves (John, y)

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)

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.

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.