C4.5: programs for machine learning
C4.5: programs for machine learning
Generating summaries from event data
Information Processing and Management: an International Journal - Special issue: summarizing text
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Building natural language generation systems
Building natural language generation systems
The Linguistic Basis of Text Generation
The Linguistic Basis of Text Generation
Using Natural-Language Processing to Produce Weather Forecasts
IEEE Expert: Intelligent Systems and Their Applications
Near-synonymy and lexical choice
Computational Linguistics
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Generating English summaries of time series data using the Gricean maxims
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SUMMAC: a text summarization evaluation
Natural Language Engineering
Design of a knowledge-based report generator
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Automatically extracting and representing collocations for language generation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Generation of extended bilingual statistical reports
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Summarizing neonatal time series data
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Suregen-2: a shell system for the generation of clinical documents
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
A two-stage model for content determination
EWNLG '01 Proceedings of the 8th European workshop on Natural Language Generation - Volume 8
Knowledge acquisition for natural language generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Learning the meaning and usage of time phrases from a parallel text-data corpus
HLT-NAACL-LWM '04 Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data - Volume 6
Grounded semantic composition for visual scenes
Journal of Artificial Intelligence Research
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Choosing the content of textual summaries of large time-series data sets
Natural Language Engineering
Providing affective information to family and friends based on social networks
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Perception-based approach to time series data mining
Applied Soft Computing
A presentation model for multimedia summaries of behavior
Proceedings of the 13th international conference on Intelligent user interfaces
Automatic Generation of Textual Summaries from Neonatal Intensive Care Data
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
That's nice... what can you do with it?
Computational Linguistics
Automatic generation of textual summaries from neonatal intensive care data
Artificial Intelligence
An architecture for data-to-text systems
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
System building cost vs. output quality in data-to-text generation
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Generating approximate geographic descriptions
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
SimpleNLG: a realisation engine for practical applications
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
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One of the main challenges in automatically generating textual weather forecasts is choosing appropriate English words to communicate numeric weather data. A corpus-based analysis of how humans write forecasts showed that there were major differences in how individual writers performed this task, that is, in how they translated data into words. These differences included both different preferences between potential near-synonyms that could be used to express information, and also differences in the meanings that individual writers associated with specific words. Because we thought these differences could confuse readers, we built our SUMTIME-MOUSAM weather-forecast generator to use consistent data-to-word rules, which avoided words which were only used by a few people, and words which were interpreted differently by different people. An evaluation by forecast users suggested that they preferred SUMTIME-MOUSAM'S texts to human-generated texts, in part because of better word choice; this may be the first time that an evaluation has shown that NLG texts are better than human-authored texts.