Fall 2004

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EECS 595 / LING 541 / SI 661
Natural Language Processing

• Fall 2004
• Lecture Notes 6

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Natural Language Generation
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What is NLG?

• Mapping meaning to text
• Stages
• Content selection
• Lexical selection
• Sentence structure aggregation, referring
  expressions
• Discourse structure

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Systemic grammars

• Language is viewed as a resource for expressing
  meaning in context (Halliday, 1985)
• Layers mood, transitivity, theme

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Example

• (process save-1actor system-1goal
  document-1speechact assertiontense future
• )
• ? Input is underspecified

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The Functional Unification Formalism (FUF)

• Based on Kays (83) formalism
• partial information, declarative, uniform,
  compact
• same framework used for all stages syntactic
  realization, lexicalization, and text planning

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Functional analysis

• Functional vs. structured analysis
• John eats an apple
• actor (John), affected (apple), process (eat)
• NP VP NP
• suitable for generation

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Partial vs. complete specification
action eat
actor John
object apple

• Voice An apple is eaten by John
• Tense John ate an apple
• Mode Did John ear an apple?
• Modality John must eat an apple
• prolog p(X,b,c)

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Unification

• Target sentence
• input FD
• grammar
• unification process
• linearization process

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Sample input
((cat s) (prot ((n ((lex john))))) (verb ((v
((lex like))))) (goal ((n ((lex mary))))))
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Sample grammar
((alt top (((cat s) (prot ((cat
np))) (goal ((cat np)))
(verb ((cat vp) (number prot
number))) (pattern (prot verb
goal))) ((cat np) (n ((cat
noun) (number number)))
(alt (((proper yes)
(pattern (n))) ((proper no)
(pattern (det n))
(det ((cat article)
(lex the))))))) ((cat vp)
(pattern (v)) (v ((cat verb))))
((cat noun)) ((cat verb))
((cat article)))))
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Sample output
((cat s) (goal ((cat np) (n ((cat noun)
(lex mary) (number goal
number))) (pattern (n)) (proper
yes))) (pattern (prot verb goal)) (prot ((cat
np) (n ((cat noun) (lex
john) (number verb number)))
(number verb number) (pattern (n))
(proper yes))) (verb ((cat vp)
(pattern (v)) (v ((cat verb)
(lex like))))))
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Comparison with Prolog

• Similarities
• both have unification at the core
• Prolog program FUF grammar
• Prolog query FUF input
• Differences
• Prolog first order term unification
• FUF arbitrarily rooted directed graphs are
  unified

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The SURGE grammar

• Syntactic realization front-end
• variable level of abstraction
• 5600 branches and 1600 alts

Lexicalized FD
Syntactic FD
LinearizerMorphology
Lexicalchooser
SURGE
Text
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Systems developed using FUF/SURGE

• COMET
• MAGIC
• ZEDDOC
• PLANDOC
• FLOWDOC
• SUMMONS

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CFUF

• Fast implementation by Mark Kharitonov (C)
• Up to 100 times faster than Lisp/FUF
• Speedup higher for larger inputs

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References

• Cole, Mariani, Uszkoreit, Zaenen, Zue (eds.)
  Survey of the State of the Art in Human Language
  Technology, 1995
• Elhadad, Using Argumentation to Control Lexical
  Choice A Functional Unification Implementation,
  1993
• Elhadad, FUF the Universal Unifier, User Manual,
  1993
• Elhadad and Robin, SURGE a Comprehensive Plug-in
  Syntactic Realization Component for Text
  Generation, 1999
• Kharitonov, CFUF A Fast Interpreter for the
  Functional Unification Formalism, 1999
• Radev, Language Reuse and Regeneration
  Generating Natural Language Summaries from
  Multiple On-Line Sources, Department of Computer
  Science, Columbia University, October 1998

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Path notation

• You can view a FD as a tree
• To specify features, you can use a path
• feature feature feature value
• e.g. prot number
• You can also use relative paths
• number value gt the feature number for the
  current node
• number value gt the feature number for the
  node above the current node

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Sample grammar
((alt top (((cat s) (prot ((cat
np))) (goal ((cat np)))
(verb ((cat vp) (number prot
number))) (pattern (prot verb
goal))) ((cat np) (n ((cat
noun) (number number)))
(alt (((proper yes)
(pattern (n))) ((proper no)
(pattern (det n))
(det ((cat article)
(lex the))))))) ((cat vp)
(pattern (v)) (v ((cat verb))))
((cat noun)) ((cat verb))
((cat article)))))
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Unification Example
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Unify Prot
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Unify Goal
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Unify vp
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Unify verb
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Finish
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Readings for next time

• JM Chapters 14, 15, 20