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presentations
Memory Searching Pathway Underlying Verb Fluency
Published:
The current study compares memory search pathway underlying verb fluency to noun (animal category) fluency using a cognitive modeling approach.
Comparing Semantic Networks of Early Vocabulary Across Languages
Published:
The present study aimed to compare the organization of early vocabulary networks across different languages.
Developing a Web-Based Stimulus Selection Hub for Anomia Treatment Using R and Shiny
Published:
The aim of this study was to develop a web-based tool that could be used by clinicians and researchers to obtain a variety of commonly used psycholinguistic properties of words. This would provide a one-stop-shop approach for users, particularly those who may not have the coding and data scraping skills or the time available for data extraction and comparison.
How Is AI Being Used Across Communication Disorders? A Review of the Research
Published:
A scoping review mapping AI research across five areas of communication disorders, using network science to identify high-impact entry points for clinicians, researchers, and educators.
publications
Improving Precision of Grammatical Error Correction with a Cheat Sheet
Published in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 2019
In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction.
Recommended citation: Qiu, M., Chen, X., Liu, M., Parvathala, K., Patil, A., & Park, J. (2019). Improving precision of grammatical error correction with a cheat sheet. Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 240-245. https://doi.org/10.18653/v1/W19-4425
Artificial Error Generation with Fluency Filtering
Published in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 2019
The present study explores how fluency filtering can affect the quality of artificial errors.
Recommended citation: Qiu, M., & Park, J. (2019). Artificial error generation with fluency filtering. Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 87–91. https://doi.org/10.18653/v1/W19-4408
Semantic Diversity in Paired-Associate Learning: Further Evidence for the Information Accumulation Perspective of Cognitive Aging
Published in Psychonomic Bulletin & Review, 2020
The current study used a within-subject design in order to examine the influence of linguistic experience on paired associate learning in younger and older adults. Linguistic experience was modeled using a semantic diversity measure of word strength. When frequency is controlled for, high semantic diversity words are associated to a greater number of words and have a higher average strength of association. In the current study, PAL performance of older adults was significantly lower for word pairs involving high semantic diversity words, while their performance did not differ for low semantic diversity words, consistent with the information accumulation perspective of aging.
Recommended citation: Qiu, M., & Johns, B. T. (2020). Semantic diversity in paired-associate learning: Further evidence for the information accumulation perspective of cognitive aging. Psychonomic Bulletin & Review, 27(1), 114–121. https://doi.org/10.3758/s13423-019-01691-w
Structural Comparisons of Noun and Verb Networks in the Mental Lexicon
Published in Proceedings of the Annual Meeting of the Cognitive Science Society, 2021
The current study uses network science tools to quantitatively investigate the structural differences of noun and verb categories.
Recommended citation: Qiu, M., Castro, N., & Johns, B. T. (2021). Structural comparisons of noun and verb networks in the mental lexicon. Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 1649-1655. https://escholarship.org/uc/item/4b20s6wp
Frustratingly Simple Prompting-based Text Denoising
Published in The Second Tiny Papers Track at ICLR 2024, 2024
Our paper underscores how making minor changes to a dataset through text denoising can enhance the final results.
Recommended citation: Park, J., & Qiu, M. (2024). Frustratingly simple prompting-based text denoising. The Second Tiny Papers Track at ICLR 2024. https://openreview.net/pdf?id=XlJRjhIkNi
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency
Published in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2024
This paper proposes an analysis of prompting strategies for grammatical error correction (GEC) with selected large language models (LLM) based on language proficiency.
Recommended citation: Zeng, M., Kuang, J., Qiu, M., Song, J., & Park, J. (2024). Evaluating prompting strategies for grammatical error correction based on language proficiency. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 6426–6430. https://aclanthology.org/2024.lrec-main.569
Estimating Type of Print Exposure Across Aging Through Author Production
Published in Proceedings of the Annual Meeting of the Cognitive Science Society, 2024
This study introduces a novel approach for quantifying individual differences in print exposure through the integration of distributional semantics with the Author Production Test (APT).
Recommended citation: Qiu, M., Castro, N., & Johns, B. T. (2024). Estimating type of print exposure across aging through author production. Proceedings of the Annual Meeting of the Cognitive Science Society, 46, 1175–1181. https://escholarship.org/uc/item/41z38291
Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology
Published in Proceedings of the 31st International Conference on Computational Linguistics, 2025
This paper introduces improvements to automatic grammatical error annotation for Chinese. Our refined framework addresses language-specific challenges that cause common spelling errors in Chinese, including pronunciation similarity, visual shape similarity, specialized participles, and word ordering.
Recommended citation: Gu, Y., Huang, Z., Zeng, M., Qiu, M., & Park, J. (2025). Improving automatic grammatical error annotation for Chinese through linguistically-informed error typology. Proceedings of the 31st International Conference on Computational Linguistics, 2781–2798. https://aclanthology.org/2025.coling-main.189/
Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach
Published in Proceedings of the 31st International Conference on Computational Linguistics, 2025
We propose a refined alignment-based method to assess end-to-end grammatical error correction (GEC) systems, aiming to reproduce and improve results from existing evaluation tools, such as ERRANT, even when applied to raw text input—reflecting real-world language learners’ writing scenarios.
Recommended citation: Wang, J., Qiu, M., Gu, Y., Huang, Z., & Park, J. (2025). Refined evaluation for end-to-end grammatical error correction using an alignment-based approach. Proceedings of the 31st International Conference on Computational Linguistics, 774–785. https://aclanthology.org/2025.coling-main.52/
A Network Analysis of the Semantic Evolution of ‘Fruit’ and ‘Stone’ in Tibeto-Burman Languages
Published in Poznan Studies in Contemporary Linguistics, 2025
The lexemes ‘fruit’ and ‘stone’ are known as the origins of the numeral classifiers for small round objects in many Tibeto-Burman languages. This paper employs a correlation-based network construction method to investigate the colexification networks of the two concepts in 58 + 68 Tibeto-Burman languages.
Recommended citation: Li, Y., & Qiu, M. (2025). A network analysis of the semantic evolution of ‘fruit’ and ‘stone’ in Tibeto-Burman languages. Poznan Studies in Contemporary Linguistics, 61(2), 121–172. https://doi.org/10.1515/psicl-2024-0024
Progress Toward Estimating the Minimal Clinically Important Difference of Intelligibility: A Crowdsourced Perceptual Experiment
Published in Journal of Speech, Language, and Hearing Research, 2025
The purpose of the current study was to estimate the minimal clinically important difference (MCID) of sentence intelligibility in control speakers and in speakers with dysarthria due to multiple sclerosis (MS) and Parkinson's disease (PD).
Recommended citation: Stipancic, K. L., van Brenk, F., Qiu, M., & Tjaden, K. (2025). Progress toward estimating the minimal clinically important difference of intelligibility: A crowdsourced perceptual experiment. Journal of Speech, Language, and Hearing Research, 68(7S), 3480–3494. https://doi.org/10.1044/2024_JSLHR-24-00354
Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework With Language-Specific Flexibility
Published in Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications, 2025
In this paper, we introduce a standardized, modular framework for multilingual grammatical error annotation.
Recommended citation: Qiu, M., Nguyen, T. M., Huang, Z., Li, Z., Gu, Y., Gao, Q., Liu, S., & Park, J. (2025). Multilingual grammatical error annotation: Combining language-agnostic framework with language-specific flexibility. Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications, 202–212. https://doi.org/10.18653/v1/2025.bea-1.15
Bias in Large Language Models: Origin, Evaluation, and Mitigation
Published in Electronics, 2026
Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible artificial intelligence (AI) systems.
Recommended citation: Guo, Y., Guo, M., Su, J., Yang, Z., Zhu, M., Li, H., Qiu, M., & Liu, S. S. (2026). Bias in large language models: Origin, evaluation, and mitigation. Electronics, 15(9), 1824. https://doi.org/10.3390/electronics15091824
Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task
Published in Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics, 2026
This study examines whether LLMs can approximate individual differences in the phonemic fluency task, where participants generate words beginning with a target letter.
Recommended citation: Qiu, M., Brisebois, Z., & Sun, S. (2026). Can LLMs simulate human behavioral variability? A case study in the phonemic fluency task. Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics, 250–263.