Computational Modelling of Quantifier Use: Corpus, Models, and Evaluation

Guanyi Chen, Kees van Deemter

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

A prominent strand of work in formal semantics investigates the ways in which human languages quantify the elements of a set, as when we say All A are B, Few A are B, and so on. Building on a growing body of empirical studies that shed light on the meaning and the use of quantifiers, we extend this line of work by computationally modelling how human speakers textually describe complex scenes in which quantitative relations play an important role. To this end, we conduct a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions. The experiments result in a corpus, called QTUNA, made up of short texts that contain a large variety of quantified expressions. We analyse QTUNA, summarise our findings, and explain how we design computational models of human quantifier use accordingly. Finally, we evaluate these models in accordance with QTUNA.
Original languageEnglish
Pages (from-to)167-206
Number of pages40
JournalJournal of Artificial Intelligence Research
Volume77
DOIs
Publication statusPublished - 30 May 2023

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