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A very short introduction to Natural Language Generation Kees van Deemter Computing Science University of Aberdeen

A very short introduction to Natural Language Generation

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A very short introduction to Natural Language Generation. Kees van Deemter Computing Science University of Aberdeen. Natural Language Understanding. Natural Language Generation. Speech Recognition. Speech Synthesis. Language Technology. Meaning. Text. Text. Speech. Speech. - PowerPoint PPT Presentation

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Page 1: A  very  short introduction to  Natural Language Generation

A very short introduction to Natural Language Generation

Kees van Deemter

Computing Science

University of Aberdeen

Page 2: A  very  short introduction to  Natural Language Generation

Text

Language Technology

Natural Language Understanding

Natural Language Generation

Speech Recognition

Speech Synthesis

Text

Meaning

Speech Speech

Page 3: A  very  short introduction to  Natural Language Generation

First: NLG from a practical perspective Goal:

Use computers to express information in human-accessible form

Input: some non-linguistic representation of information (e.g.,

tables in database, logical formulas, JAVA code, ...) Output:

documents, reports, explanations, help messages, ... in some human language (Chinese, English, Dutch)

Knowledge sources required: knowledge of language and of the domain; maybe of the

intended audience as well

Page 4: A  very  short introduction to  Natural Language Generation

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. [Kitteridge, Goldberg and Driedger 1994.]

Status: Fielded, in operational use since 1992

Page 5: A  very  short introduction to  Natural Language Generation

FoG: Input

Page 6: A  very  short introduction to  Natural Language Generation

FoG: Output

Page 7: A  very  short introduction to  Natural Language Generation

Example System: Dial Your Disc (DYD) Function:

Context-sensitive descriptions of Mozart’s instrumental music

Input: Music database + history of interaction

Target user: Music industry, customers for music-on-demand

Developer: Philips Electronics (Nat Lab – IPO, Eindhoven; 1993-6)

[Van Deemter & Odijk 1995] Status:

Methods reused in GOALGETTER and other systems

Page 8: A  very  short introduction to  Natural Language Generation

Example System: Dial Your Disc (DYD)

User composes a home-made CD Speech interface tells system what type of music

the user would like to add to the CD. E.g., “I’d like some piano music”. “I’m interested in solo

performances”. “piano”, “solo” System chooses one composition with solo piano.

The music starts. After a while, a text is spoken The second time a piano sonata is selected, the

following text might be generated:

Page 9: A  very  short introduction to  Natural Language Generation

Example System: Dial Your Disc (DYD)

Example of approximate output, in its most elaborate form:

“The following+ composition+, from which you are going to hear a fragment+ of part three+, was written+ by Mozart in the beginning+ of seventeen+ seventy+ five+, in Munich+. The work is also+ a sonata+ in f+, like the preceding+ composition, but now+ for piano+. The KV+ number of this work is K. two+ eight+ zero+. This sonata+ consists of three+ parts+: allegro assai+, adagio+, and presto+. The presto lasts two+ minutes+ forty+ five+ seconds+. This presto is located on track six+ of first+ CD+ of volume seventeen+. The piano+ is played by Mitsuko Uchida+. The recording+ of the sonata+ was made+ in the Henry Wood+ Hall in London+, England, in the eighties+. The quality+ of its recording is DDD+. The following+ is a fragment+ of the third+ part+.” [A fragment follows] Each “+” marks a pitch accent on the preceding word

Page 10: A  very  short introduction to  Natural Language Generation

When to use NLG?When

there are many potential documents to be written, differing according to the context (user, situation, language)

there are some general principles behind document design.

Page 11: A  very  short introduction to  Natural Language Generation

Why is NLG hard? NLG involves many choices, e.g. which

content to include, what order to say it in, what words to use.

Linguistics does not yet provide us with a ready-made, precise theory about how to make such choices to produce coherent text

Page 12: A  very  short introduction to  Natural Language Generation

Why does choice matter?

The Serbian Prime Minister, Zoran Djindjic, has been assassinated in the capital, Belgrade.

The pro-reform, pro-Western leader was shot in the stomach and in the back outside government offices at around 1300 (1200 gmt), and died of his wounds in hospital.

(BBC news, UK edition, 12/3/03)

Page 13: A  very  short introduction to  Natural Language Generation

Tasks and Architecture in NLG (Reiter 1994)

Content Determination

Document Structuring

Aggregation

Lexicalisation

Generation of Referring Expressions

Linguistic Realisation

Physical Realisation

Document Planning

Micro-planning

Surface Realisation

Page 14: A  very  short introduction to  Natural Language Generation

Second perspective: NLG as a branch of linguistics

NLG systems map ideas to words Surely, this is linguistic territory!

If linguists cannot say how the different stories about James Sportler differ, then who can?

An NLG program might be seen as a model of language production (in terms of its output; the human production process may be very different)

Page 15: A  very  short introduction to  Natural Language Generation

NLG is the smaller twin brother of NL Understanding

NLG poses deep theoretical problems about language and communication

NLG has great potential for applications

This course: Generation of Referring Expressions

Page 16: A  very  short introduction to  Natural Language Generation