TAILIEUCHUNG - Báo cáo khoa học: "Generating Spatio-Temporal Descriptions in Pollen Forecasts"

We describe our initial investigations into generating textual summaries of spatiotemporal data with the help of a prototype Natural Language Generation (NLG) system that produces pollen forecasts for Scotland. forecasts were written. An example of a pollen forecast text is shown in Figure 1, its corresponding data is shown in table 1. A pollen forecast in the map form is shown in Figure 2. ‘Monday looks set to bring another day of relatively high pollen counts, with values up to a very high eight in the Central Belt. . | Generating Spatio-Temporal Descriptions in Pollen Forecasts Ross Turner Somayajulu Sripada and Ehud Reiter Ian P Davy Dept of Computing Science Aerospace and Marine International University of Aberdeen UK Banchory Aberdeenshire UK rturner ssripada ereiter @ idavy@ Abstract We describe our initial investigations into generating textual summaries of spatiotemporal data with the help of a prototype Natural Language Generation NLG system that produces pollen forecasts for Scotland. 1 Introduction New monitoring devices such as remote sensing systems are generating vast amounts of spatio-temporal data. These devices coupled with the wider accessibility of the data have spurred large amounts of research into how it can best be analysed. There has been less research however into how the results of the data analysis can be effectively communicated. As part of a wider research project aiming to produce textual reports of complex spatio-temporal data we have developed a prototype NLG system which produces textual pollen forecasts for the general public. Pollen forecast texts describe predicted pollen concentration values for different regions of a country. Their production involves two subtasks predicting pollen concentration values for different regions of a country and describing these numerical values our work we focus on the later subtask textual description of spatio-temporally distributed pollen concentration values. The subtask of predicting pollen concentrations is carried out by our industrial collaborator Aerospace and Marine International UK Ltd AMI . A fairly substantial amount of work already exists on weather forecast generation. A number of systems have been developed and are currently in commercial use with two of the most notable being FOG Goldberg etal. 1994 and MultiMeteo Coch 1998 . 2 Knowledge Acquisition Our knowledge acquisition activities consisted of corpus studies and discussions with experts. We have .

• Báo cáo khoa học
• Generating Spatio Temporal Descriptions
• Pollen Forecasts
• Ross Turner
• Somayajulu Sripada
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln