Language Technology
description
Transcript of Language Technology
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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
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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]
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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
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FoG: Input
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FoG: Output
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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
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TEMSIShttp://www.dfki.de/service/nlg-demo/
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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.
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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
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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))))
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Basic Generation Problem
• How to go from an abstract semantic input to a concrete linguistic form that is
– semantically correct– stylistically appropriate– textually appropriate
???
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Standard Pipelined Architecture
Document Planning
Microplanning
Surface Realisation
Document Plan
Text Specification
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KPMLlexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATOR
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KPMLlexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATORKPML is a
Resources
Processgeneration
engine
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lexicogrammar
semantics
sentence
Semantic specification
TACTICAL GENERATION
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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]
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syntagmatic
Linguistic Description with system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finiteparadigmatic
AXES
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lexicogrammar
Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
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Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
grammaticalsystems
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Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
grammaticalfeatures
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Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Resource Architecture in KPML:system networks
imperative
indicative
interrogative
declarative
realizationstatements
+Finite
Finite^Subject
Subject^Finite
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Generation Process:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:system networks
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
imperative
indicative
interrogative
declarative+Finite
Finite^Subject
Subject^Finite
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Generation Process:traversal
indicative
interrogative
+Finite
Finite^Subject
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Generation Process:structure
+Finite
Finite^Subjectinterrogative
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Generation Process:structure
+Finite
Finite^Subject
interrogative
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Immediate Dominance
Linear Precedence
Generation Process:realization statements
+Finite
Finite^Subject SubjectFinite
[clause]
Are you going?[interrogative]
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Types of Realization Statements
• Ordering (immediate, relative)• Structure building• Lexicalization
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Functionally Motivated Grammatical
Choices
USER
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Functionally Motivated Grammatical
Choices
USER
user = language engineer:developing and debugging the “grammatical competence”of a language resource
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Functionally Motivated Grammatical
Choices
USER
SemanticSpecifications
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Functionally Motivated Grammatical
Choices
USER
user = system builder:developing and debugging asystem that expects naturallanguage generationfunctionality
SemanticSpecifications