From Dependency to CCG to Incremental CCG: Approaches for Flexible Word Order in Turkish

Özge Bakay, Oğuz Kerem Yıldız, Rajesh Bhatt, Brian Dillon, Olcay Taner Yildiz

Keywords: CCG, universal dependencies, incrementality, Turkish

TL;DR: We present the widest-coverage Turkish CCGbank to date, along with an example on how to use it to model psycholinguistic data.

Abstract: Combinatory Categorial Grammar (CCG), a lexicalized formalism known for its flexible constituency, is well-suited for modeling headfinal languages with flexible word order like Turkish. Building on Kuzgun et al. (2023), we first develop a Turkish CCG lexicon by automatically inducing categories from a dependency treebank. By leveraging standard and extended operations tailored to Turkish syntax, our parser achieves a robust coverage of 92.5%. Furthermore, we introduce the first (partially) incremental, left-to-right CCG parser for Turkish, designed to facilitate the immediate integration of words into the evolving representation. Finally, we present an example experiment showing that CCG parsers can model psycholinguistic evidence for extra processing costs associated with arguments in noncanonical positions, via the frequency of order-reversing operations. These findings provide evidence that CCG offers a cognitively plausible framework for modeling real-time processing in languages like Turkish.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Resources and Tools for Scientifically Motivated Research

Secondary Area Selection: Computational Psycholinguistics, Cognition and Linguistics

Use Of Generative Artificial Intelligence Tools: Yes, for editing/proofreading the manuscript

Data Collection From Human Subjects: No

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 165