Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch

TL;DR

This thesis develops a dependency-enhanced type lexicon for Dutch using type-logical grammars based on linear logic, implementing algorithms and neural models to improve syntactic and semantic parsing of variable word order sentences.

Contribution

It introduces a novel dependency-based type lexicon for Dutch and a neural sequence transduction model that learns type assignment, enhancing parsing and linguistic analysis.

Findings

Neural model successfully learns type assignment from data.

Deductive parser efficiently resolves structural ambiguities.

Type grammar handles Dutch word order variability effectively.

Abstract

This thesis is concerned with type-logical grammars and their practical applicability as tools of reasoning about sentence syntax and semantics. The focal point is narrowed to Dutch, a language exhibiting a large degree of word order variability. In order to overcome difficulties arising as a result of that variability, the thesis explores and expands upon a type grammar based on Multiplicative Intuitionistic Linear Logic, agnostic to word order but enriched with decorations that aim to reduce its proof-theoretic complexity. An algorithm for the conversion of dependency-annotated sentences into type sequences is then implemented, populating the type logic with concrete, data-driven lexical types. Two experiments are ran on the resulting grammar instantiation. The first pertains to the learnability of the type-assignment process by a neural architecture. A novel application of a self-attentive sequence transduction model is proposed; contrary to established practices, it constructs types inductively by internalizing the type-formation syntax, thus exhibiting generalizability beyond a pre-specified type vocabulary. The second revolves around a deductive parsing system that can resolve structural ambiguities by consulting both word and type information; preliminary results suggest both excellent computational efficiency and performance.