Natural Language Generation and Fuzzy Sets: An Exploratory Study on Geographical Referring Expression Generation
We explore how the problem of uncertainty and imprecision in natural language generation (NLG) could be addressed through the use of fuzzy sets. We propose bringing together standard empirical procedures for knowledge acquisition in NLG and computing with words/perceptions related techniques (with a special focus on linguistic description of data) to address an open challenge in NLG: the generation of geographical referring expressions. Following this methodology, we present an exploratory experiment which provides some insights about how human subjects refer to geographical expressions and discuss how the obtained results might relate to the use of fuzzy sets.
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Publication: Congress
1624015040255
June 18, 2021
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We explore how the problem of uncertainty and imprecision in natural language generation (NLG) could be addressed through the use of fuzzy sets. We propose bringing together standard empirical procedures for knowledge acquisition in NLG and computing with words/perceptions related techniques (with a special focus on linguistic description of data) to address an open challenge in NLG: the generation of geographical referring expressions. Following this methodology, we present an exploratory experiment which provides some insights about how human subjects refer to geographical expressions and discuss how the obtained results might relate to the use of fuzzy sets. - Alejandro Ramos Soto, Nava Tintarev, Rodrigo de Oliveira, Ehud Reiter, Kees van Deemter - 10.1109/FUZZ-IEEE.2016.7737740
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