Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers

Alejandro  Ramos; Jose M. Alonso; Ehud Reiter; Kees Van Deemter; Albert Gatt

doi:10.2991/ijcis.d.190826.002

Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers

Alejandro Ramos, Jose M. Alonso^* (Corresponding Author), Ehud Reiter, Kees Van Deemter, Albert Gatt

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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 eographical expressions grounded on the models.

Original language	English
Pages (from-to)	970 - 983
Number of pages	14
Journal	International Journal of Computational Intelligence Systems
Volume	12
Issue number	2
Early online date	12 Sep 2019
DOIs	https://doi.org/10.2991/ijcis.d.190826.002
Publication status	Published - 2019

Keywords

natural language generation
linguistic descriptions of data
data-to-text
geo-referenced data
language grounding
fuzzy sets
Fuzzy sets
Geo-referenced data
Data-to-text
Linguistic descriptions of data
Natural language generation
Language grounding

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10.2991/ijcis.d.190826.002Licence: CC BY-NC

Ramos_etal_Fuzzy_Based_Language_VOR
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Final published version, 3.12 MBLicence: CC BY-NC

Cite this

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title = "Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers",

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 eographical expressions grounded on the models.",

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author = "Alejandro Ramos and Alonso, {Jose M.} and Ehud Reiter and {Van Deemter}, Kees and Albert Gatt",

note = "Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802). This research was also funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-BI00, TIN2017-84796-C2-1-R and TIN2017-90773-REDT) and the Galician Ministry of Education, University and Professional Training (grants ED431F2018/02, ED431C 2018/29 and “accreditation 2016-2019, ED431G/08”). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).",

year = "2019",

doi = "10.2991/ijcis.d.190826.002",

language = "English",

volume = "12",

pages = "970 -- 983",

journal = "International Journal of Computational Intelligence Systems",

issn = "1875-6891",

publisher = "Atlantis Press SARL",

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AU - Reiter, Ehud

AU - Van Deemter, Kees

AU - Gatt, Albert

N1 - Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802). This research was also funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-BI00, TIN2017-84796-C2-1-R and TIN2017-90773-REDT) and the Galician Ministry of Education, University and Professional Training (grants ED431F2018/02, ED431C 2018/29 and “accreditation 2016-2019, ED431G/08”). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).

PY - 2019

Y1 - 2019

N2 - 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 eographical expressions grounded on the models.

AB - 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 eographical expressions grounded on the models.

KW - natural language generation

KW - linguistic descriptions of data

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KW - geo-referenced data

KW - language grounding

KW - fuzzy sets

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KW - Data-to-text

KW - Linguistic descriptions of data

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KW - Language grounding

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JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

SN - 1875-6891

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ER -

Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers

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