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Séminaire Donnees et APprentissage ArtificielRSS

Fuzzy Semantic Sentence Similarity Measures
报告人 : Keeley Crockett (University of Manchester, UK)
A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent perception based (fuzzy) words that are commonly used in natural language. Given the wide use of fuzzy words in natural language this limits the strength of these measures in the areas where they are practically applied. This talk briefly reviews traditional semantic word and sentence similarity measures and then describes a new fuzzy measure known as **FAST** (Fuzzy Algorithm for Similarity Testing). FAST is an ontology based similarity measure that uses concepts of fuzzy logic and computing with words to allow for the accurate representation of fuzzy based words. Through empirical human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words. These relationships allowed for the creation of a new ontology based semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. Initial experiments using FAST are described on two possible future benchmark "fuzzy" datasets. The results show that there was an improved level of correlation between FAST and human test results compared with two traditional sentence similarity measures.
The talk concludes by looking at one potential application area where semantic similarity measures are utilised in a Student Debt Advisor Conversational Agent to remove the need for extensive scripting and maintenance.
benjamin.piwowarski (at)
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