Fall 2004

Lecture Notes #6

EECS 595 / LING 541 / SI 661

Natural Language Processing

Natural Language Generation

What is NLG?

• Mapping meaning to text

• Stages:– Content selection– Lexical selection– Sentence structure: aggregation, referring

expressions– Discourse structure

Acrobat Document

Systemic grammars

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

• Layers: mood, transitivity, theme

The system will save the document

Mood subject finite predicator object

Transitivity actor process goal

Theme theme rheme

Acrobat Document

Example

(:process save-1:actor system-1:goal document-1:speechact assertion:tense future

) Input is underspecified

The Functional Unification Formalism (FUF)

• Based on Kay’s (83) formalism

• partial information, declarative, uniform, compact

• same framework used for all stages: syntactic realization, lexicalization, and text planning

Functional analysis

• Functional vs. structured analysis

• “John eats an apple”

• actor (John), affected (apple), process (eat)

• NP VP NP

• suitable for generation

Partial vs. complete specification

• 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)

action = eat

actor = John

object = apple

Unification

• Target sentence

• input FD

• grammar

• unification process

• linearization process

Sample input

((cat s) (prot ((n ((lex john))))) (verb ((v ((lex like))))) (goal ((n ((lex mary))))))

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)))))

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))))))

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

The SURGE grammar• Syntactic realization front-end

• variable level of abstraction

• 5600 branches and 1600 alts

Lexicalchooser

SURGELinearizerMorphology

Lexicalized FD Syntactic FD

Text

Systems developed using FUF/SURGE

• COMET

• MAGIC

• ZEDDOC

• PLANDOC

• FLOWDOC

• SUMMONS

CFUF

• Fast implementation by Mark Kharitonov (C++)

• Up to 100 times faster than Lisp/FUF

• Speedup higher for larger inputs

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

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 => the feature number for the current

node

– {^ ^ number} value => the feature number for the node above the current node

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)))))

Unification Example

Unify Prot

Unify Goal

Unify vp

Unify verb

Finish

Readings for next time

• J&M Chapters 14, 15, 20