COMP 4060 Natural Language Processing

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COMP 4060 Natural Language Processing

• Semantics

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Semantics What do we need?

• 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)
• Description Logic (DL) and similar KR languages
• Ontologies

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Constructing a Semantic Representation

• General approach
• 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).

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Semantic Representation

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

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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

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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

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Constructing an InterLingua 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.

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Basic Semantic Dependency - Example
Input John makes tools Syntactic Analysis
cat verb root make tense present subject
  root john cat noun-proper object   roo
t     tool cat noun number plural
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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
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Ontological Representation - Example
Relevant extract from the specification of the
ontological concept used to describe the
appropriate meaning of make manufacturing-activi
ty... agent human theme artifact
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Semantic Dependency Component
The basic semantic dependency component of the
Text Meaning Representation (TMR) for John
makes tools manufacturing-activity-7 agent human
-3 theme set-1 element tool cardinality gt
1
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semantic representation of try-v3
try-v3 syn-struc root try cat v subj
root var1 cat n xcomp root
var2 cat v form OR infinitive
gerund sem-struc set-1 element-type refsem-1
cardinality gt1 refsem-1 sem event agent
var1 effect refsem-2 modality modality-
type epiteuctic modality-scope refsem-2 mod
ality-value lt 1 refsem-2 value var2 sem ev
ent
Means non finished action outcome unclear
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Why is Iraq developing weapons of mass
destruction?
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Wordsense Disambiguation

• Methods
• Constraint checking
• make 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 one
• Hunter-gatherer processing
• find (hunt) and eliminate (kill) unlikely
  interpretations
• collect (gather) remaining interpretations

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Logic Formalisms

• Lambda Calculus

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Semantics - Lambda Calculus 1

• Logic representations often involve
  Lambda(?)-Calculus
• ?-expressions represent central phrases (e.g. VP)
  
• They are like functions which can be applied to
  terms
• We replace variables in ?-expression with
  semantic representations of complements or
  modifier phrases

?x,y loves (x, y) FOPL sentence ?x?y loves (x,
y) ?-expression ?x?y loves (x, y) (John) ? ?y
loves (John, y) function
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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) ? xAI Caramba ?
  yICSI
• Lambda Conversion
• ?x?y close-to (x, y) (AI Caramba)
• Lambda Reduction
• ?y close-to (AI Caramba, y)
• close-to (AI Caramba, ICSI)

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Semantics - Lambda Calculus 3

• Lambda-expressions can be constructed from
  central expression (VP), 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.
  Sentence represents concrete event e.
• Fill in appropriate expressions for x, y derived
  from the complements / syntactic features of verb
  serve in sentence.
• For example,AI Caramba serves meat.
• - object-NP meat for x and
• - subject-NP Al Caramba for y.

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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.
• Nirenburg, S. V. Raskin, Ontological Semantics,
  MIT Press, 2004.
• Wordnet, http//wordnet.princeton.edu/
• Suggested Upper Merged Ontology (SUMO),
  http//www.ontologyportal.org/