Theory-driven statistical modeling for semantics and pragmatics

A case study on grammatically generated implicature readings

Bibliographic Data

ID3040201
AuthorsMichael Franke (0000-0001-9670-8510), Lori Bergen (0009-0001-1445-5247), Leon Bergen
Year2020
Volume96
Issue2
Pagese77-e96
Publication date2020-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLanguage (JOURNAL)
Journal identifiersISSN: 0097-8507 • E-ISSN: 1535-0665
PublisherProject MUSE (PUBLISHER • US)
DOI10.1353/lan.2020.0034
OpenAlexW3036874244
LanguageEN
Citations received13

Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in which grammatically generated candidate readings are taken into account in four probabilistic pragmatic models of utterance and interpretation choice. The data provide strong evidence for the idea that the full range of potential readings made available by recently popular grammatical approaches to scalar-implicature computation might be needed, and that classical Gricean reasoning may help manage the manifold ambiguity introduced by grammatical approaches to these. The case study thereby shows a way of bridging linguistic theory and empirical data with the help of probabilistic pragmatic modeling as a linking function

Ambiguity · Computational linguistics · Implicature · Interpretation (philosophy) · Linguistics · Natural language processing · Pragmatics · Probabilistic logic · Semantics (computer science) · Statistical model · Utterance · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Speech and dialogue systems · Topic Modeling

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