The Causal Structure of Semantic Ambiguities

TL;DR

This paper introduces a formal sheaf-theoretic model to analyze semantic ambiguities in natural language, focusing on causal structures and plausibility, supported by empirical data from psycholinguistic experiments.

Contribution

It applies a novel sheaf-theoretic causality model to semantic ambiguities, integrating empirical plausibility data with formal causal reasoning.

Findings

Identified two main disambiguation orders: subject-verb and object-verb.

Discovered delays in disambiguating polysemous versus homonymous verbs.

Validated model predictions with psycholinguistic plausibility judgments.

Abstract

Ambiguity is a natural language phenomenon occurring at different levels of syntax, semantics, and pragmatics. It is widely studied; in Psycholinguistics, for instance, we have a variety of competing studies for the human disambiguation processes. These studies are empirical and based on eye-tracking measurements. Here we take first steps towards formalizing these processes for semantic ambiguities where we identified the presence of two features: (1) joint plausibility degrees of different possible interpretations, (2) causal structures according to which certain words play a more substantial role in the processes. The novel sheaf-theoretic model of definite causality developed by Gogioso and Pinzani in QPL 2021 offers tools to model and reason about these features. We applied this theory to a dataset of ambiguous phrases extracted from Psycholinguistics literature and their human plausibility judgements collected by us using the Amazon Mechanical Turk engine. We measured the causal fractions of different disambiguation orders within the phrases and discovered two prominent orders: from subject to verb in the subject-verb and from object to verb in the verb object phrases. We also found evidence for delay in the disambiguation of polysemous vs homonymous verbs, again compatible with Psycholinguistic findings.