dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Deemter, C.J. van | |
dc.contributor.author | Kuczuk Modenezi, Igor | |
dc.date.accessioned | 2022-09-23T00:00:31Z | |
dc.date.available | 2022-09-23T00:00:31Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42834 | |
dc.description.abstract | Logic languages such as First Order Logic can describe complex ideas, with the downside of being too
abstract. In order to make them clearer, it is possible to translate sentences in them to Natural Language. In
order to examine this kind of system further, we chose to focus on one domain: Tarski’s World. We created
a system that has a First Order Logic formula that has a quantifier as input and an English sentence as
output. Our focus was generating a more natural-sounding sentence and explore the quality of the sentence
generated. This was possible through the development of two metrics: Naturalness (how natural a sentence
is) and Clarity (how grammatically clear a sentence is). Both showed promising results but were ultimately
flawed and required further improvements. During the development of the metrics, it became apparent that
this type of sentence construction presents scope ambiguity. In order to determine the acceptable readings,
a survey was conducted. From the results we concluded a few things: that sentences with adjectives had
nocuous ambiguity; quantifiers do not present scope ambiguity; and sentences that contain numbers instead
of quantifiers present scope ambiguity. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Creation of a First-Order Logic to Natural Language generation system and development of quality metrics. The domain used is Tarski's World | |
dc.title | Generating English explanations of logical formulas: measuring the quality of the generated sentences | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | FOL; Tarski's World; Natural Language generation; metrics | |
dc.subject.courseuu | Artificial Intelligence | |
dc.thesis.id | 10802 | |