Abstract
We describe an applied methodology to build fuzzy models of geographical expressions, which are meant to be used for natural language generation purposes. Our approach encompasses a language grounding task within the development of an actual data to- text system for the generation of textual descriptions of live weather data. For this, we gathered data from meteorologists through a survey and built consistent fuzzy models that aggregate the interpersonal variations found among the experts. A subset of the models was utilized in an illustrative use case, where we generated linguistic descriptions of weather maps for specific geographical expressions. These were used in a task-based evaluation to determine how well potential readers are able to identify the geographical expressions grounded on the models.
Original language | English |
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Pages (from-to) | 970-983 |
Number of pages | 14 |
Journal | International Journal of Computational Intelligence Systems |
Volume | 12 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Funding
This research was also funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R and TIN2017-90773-REDT) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29 and “accreditation 2016-2019, ED431G/08”). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).
Keywords
- Data-to-text
- Fuzzy sets
- Geo-referenced data
- Language grounding
- Linguistic descriptions of data
- Natural language generation