Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers
- Alejandro Ramos1, Jose M. Alonso1, *, Ehud Reiter2, Kees van Deemter2, 3, Albert Gatt41 Centro Singular de Investigación en Tecnoloxías Intelixentes, Universidade de Santiago de Compostela, Santiago de Compostela, Spain2 Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom3 Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands4 Institute of Linguistics and Language Technology, University of Malta, Malta*Corresponding author. Email: email@example.com
- Corresponding Author
- Jose M. Alonso
- https://doi.org/10.2991/ijcis.d.190826.002How to use a DOI?
- Natural language generation, Linguistic descriptions of data, Data-to-text, Geo-referenced data, Language grounding, Fuzzy sets
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.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Alejandro Ramos AU - Jose M. Alonso AU - Ehud Reiter AU - Kees van Deemter AU - Albert Gatt PY - 2019 DA - 2019/09 TI - Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers JO - International Journal of Computational Intelligence Systems SP - 970 EP - 983 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190826.002 DO - https://doi.org/10.2991/ijcis.d.190826.002 ID - Ramos2019 ER -