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 datato-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.

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

“…Fuzzy logic and NLP are interconnected [11] to solve tasks such as text addition, sentiment analysis, and knowledge representation. This is because fuzzy logic overcomes the problem of inaccuracy and ambiguity of human language by providing a description of the dataset with a linguistic concept defined as a fuzzy set [12]. The following are some of the works that use fuzzy logic in text summarization.…”