Text Language Technology Natural Language Understanding Natural Language Generation Speech...
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Text Language Technology Natural Language Understanding Natural Language Generation Speech Recognition Speech Synthesis Text Meaning Speech Speech
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Text
Language Technology
Natural Language Understanding
Natural Language Generation
Speech Recognition
Speech Synthesis
Text
Meaning
Speech Speech
Text
Language Technology
Natural Language Understanding
Natural Language Generation
Speech Recognition
Speech Synthesis
Text
Meaning
Speech Speech
What is NLG?
Natural language generation is the process of deliberately constructing a natural language text in order to meet specified communicative goals.
[McDonald 1992]
Example System: FoG• Function:
– Produces textual weather reports in English and French
• Input: – Graphical/numerical weather depiction
• User: – Environment Canada (Canadian Weather Service)
• Developer: – CoGenTex
• Status: – Fielded, in operational use since 1992
FoG: Input
FoG: Output
Example System: TEMSIS
• Function: – Summarises pollutant information for environmental officials
• Input: – Environmental data + a specific query
• User: – Regional environmental agencies in France and Germany
• Developer: – DFKI GmbH
• Status: – Prototype developed; requirements for fielded system being analysed
TEMSIShttp://www.dfki.de/service/nlg-demo/
TEMSIS: Output Summary
• Le 21/7/1998 à la station de mesure de Völklingen -City, la valeur moyenne maximale d'une demi-heure (Halbstundenmittelwert) pour l'ozone atteignait 104.0 µg/m³. Par conséquent, selon le decret MIK (MIK-Verordnung), la valeur limite autorisée de 120 µg/m³ n'a pas été dépassée.
• Der höchste Halbstundenmittelwert für Ozon an der Meßstation Völklingen -City erreichte am 21. 7. 1998 104.0 µg/m³, womit der gesetzlich zulässige Grenzwert nach MIK-Verordnung von 120 µg/m³ nicht überschritten wurde.
A further system
• ILEX– generation of virtual museum information
online– http://www.hcrc.ed.ac.uk/ilex/demos/museum.cgi
• SUMTIME– generation of weather reports– http://www.csd.abdn.ac.uk/~ssripada/cgi_bin/StartSMT.html
TEMSIS: Input Query
((LANGUAGE FRENCH)(GRENZWERTLAND GERMANY)(BESTAETIGE-MS T)(BESTAETIGE-SS T)(MESSSTATION \"Voelklingen City\")(DB-ID \"#2083\")(SCHADSTOFF \"#19\")(ART MAXIMUM)(ZEIT ((JAHR 1998) (MONAT 7) (TAG 21))))
Basic Generation Problem
• How to go from an abstract semantic input to a concrete linguistic form that is
– semantically correct– stylistically appropriate– textually appropriate
???
Standard Pipelined Architecture
Document Planning
Microplanning
Surface Realisation
Document Plan
Text Specification
KPMLlexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATOR
KPMLlexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATORKPML is a
Resources
Processgeneration
engine
lexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATION
What is NLG?
Natural language generation is the process of deliberately constructing a natural language text in order to meet specified communicative goals.
NLG is a process of choice under specified constraints
[McDonald]
syntagmatic
Linguistic Description with system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finiteparadigmatic
AXES
lexicogrammar
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
grammaticalsystems
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
grammaticalfeatures
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
realizationstatements
+Finite
Finite^Subject
Subject^Finite
Generation Process:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
Generation Process:traversal
indicative
interrogative
+Finite
Finite^Subject
Generation Process:structure
+Finite
Finite^Subjectinterrogative
Generation Process:structure
+Finite
Finite^Subject
interrogative
Immediate Dominance
Linear Precedence
Generation Process:realization statements
+Finite
Finite^Subject SubjectFinite
[clause]
Are you going?
[interrogative]
Types of Realization Statements
• Ordering (immediate, relative)
• Structure building
• Lexicalization
Functionally Motivated Grammatical
Choices
USER
Functionally Motivated Grammatical
Choices
USER
user = language engineer:developing and debugging the “grammatical competence”of a language resource
Functionally Motivated Grammatical
Choices
USER
SemanticSpecifications
Functionally Motivated Grammatical
Choices
USER
user = system builder:developing and debugging asystem that expects naturallanguage generationfunctionality
SemanticSpecifications
