This page includes a list of works that I have done or collaborated on. You can download the documents to read them in full. Feel free to email me if a link breaks or if you want a copy of any materials.
This paper proposes a formal model of regular languages enriched with unbounded copying. We augment finite-state machinery with the ability to recognize copied strings by adding an unbounded memory buffer with a restricted form of first-in-first-out storage. The newly introduced computational device, finite-state buffered machines (FSBMs), characterizes the class of regular languages and languages derived from them through a primitive copying operation. We name this language class regular copying languages (RCLs). We prove a pumping lemma and examine the closure properties of this language class. As suggested by previous literature (Gazdar and Pullum 1985, p.278), regular copying languages should approach the correct characteriza-tion of natural language word sets.
Wang, Yang, and Tim Hunter. 2023. On Regular Copying Languages. Journal of Language Modelling 11 (1):1–66. https://doi.org/10.15398/jlm.v11i1.342.
Total reduplication is common in natural language phonology and morphology. However, formally as copying on reduplicants of unbounded size, unrestricted total reduplication requires computational power beyond context-free, while other phonological and morphological patterns are regular, or even sub-regular. Thus, existing language classes characterizing reduplicated strings inevitably include typologically unattested context-free patterns, such as reversals. This paper extends regular languages to incorporate reduplication by introducing a new computational device: finite state buffered machine (FSBMs). We give its mathematical definitions and discuss some closure properties of the corresponding set of languages. As a result, the class of regular languages and languages derived from them through a copying mechanism is characterized. Suggested by previous literature, this class of languages should approach the characterization of natural language word sets.
Wang, Yang. 2021. Recognizing Reduplicated Forms: Finite-State Buffered Machines. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 177–187, Online. Association for Computational Linguistics.
Presentations
2022
Learning underlying representations: Expectation-Maximization and the KK-Hierarchy.
Wang, Yang and Bruce Hayes. 2022. Learning underlying representations: Expectation-Maximization and the KK-Hierarchy. Poster presented at the Annual Meeting on Phonology. UCLA, Los Angeles, CA. October 2022.
Inductive bias in learning partial reduplication: Evidence from artificial grammar learning
Wilson, Colin and Yang Wang. 2022. Inductive bias in learning partial reduplication: Evidence from artificial grammar learning. Slides of a talk given at the Annual Meeting on Phonology. UCLA, Los Angeles, CA. October 2022.
2021
A formal model for recognizing reduplication
Yang Wang
Poster presented at the Annual Meeting on Phonology, Oct 2021
Wang, Yang. 2024. Studies in morphophonological copying: Analysis, experimentation and modeling. Doctoral dissertation, University of California, Los Angeles.
2021
Regular languages extended with reduplication: Formal models, proofs and illustrations
Yang Wang
Master's thesis, University of California, Los Angeles, Aug 2021
Wang, Yang. 2021. Regular Languages Extended with Reduplication: Formal Models, Proofs and Illustrations. Master’s thesis, University of California, Los Angeles.