Choosing words in computer-generated weather forecasts
| Autor: | Jim Hunter, Ian P. Davy, Ehud Reiter, Somayajulu Sripada, Jin Yu |
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| Rok vydání: | 2005 |
| Předmět: |
Lexical choice
Linguistics and Language Computer science business.industry Natural language processing Idiolect Word choice Natural language generation computer.software_genre Semantics Language and Linguistics Task (project management) Language and the word Weather forecasts Artificial Intelligence Weather data Artificial intelligence Information presentation business computer Generator (mathematics) |
| Zdroj: | Artificial Intelligence. 167(1-2):137-169 |
| ISSN: | 0004-3702 |
| DOI: | 10.1016/j.artint.2005.06.006 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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