Chaak Ming Lau

Also published as: Chaak-ming Lau


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The Extraction and Fine-grained Classification of Written Cantonese Materials through Linguistic Feature Detection
Chaak-ming Lau | Mingfei Lau | Ann Wai Huen To
Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024

This paper presents a linguistically-informed, non-machine-learning tool for classifying Written Cantonese, Standard Written Chinese, and the intermediate varieties used by Cantonese-speaking users from Hong Kong, which are often grouped into a single “Traditional Chinese” label. Our approach addresses the lack of textual materials for Cantonese NLP, a consequence of a lower sociolinguistic status of Written Cantonese and the interchangeable use of these varieties by users without sufficient language labeling. The tool utilizes key strings and quotation markers, which can be reduced to string operations, to effectively extract Written Cantonese sentences and documents from materials mixed with Standard Written Chinese. This allows for the flexible and efficient extraction of high-quality Cantonese data from large datasets, catering to specific classification needs. This implementation ensures that the tool can process large amounts of data at a low cost by bypassing model-inferencing, which is particularly significant for marginalized languages. The tool also aims to provide a baseline measure for future classification systems, and the approach may be applicable to other low-resource regional or diglossic languages.

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Multi-Tiered Cantonese Word Segmentation
Charles Lam | Chaak-ming Lau | Jackson L. Lee
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Word segmentation for Chinese text data is essential for compiling corpora and any other tasks where the notion of “word” is assumed, since Chinese orthography does not have conventional word boundaries as languages such as English do. A perennial issue, however, is that there is no consensus about the definition of “word” in Chinese, which makes word segmentation challenging. Recent work in Chinese word segmentation has begun to embrace the idea of multiple word segmentation possibilities. In a similar spirit, this paper focuses on Cantonese, another major Chinese variety. We propose a linguistically motivated, multi-tiered word segmentation system for Cantonese, and release a Cantonese corpus of 150,000 characters word-segmented by this proposal. Our work will be of interest to researchers whose work involves Cantonese corpus data.


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PyCantonese: Cantonese Linguistics and NLP in Python
Jackson Lee | Litong Chen | Charles Lam | Chaak Ming Lau | Tsz-Him Tsui
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper introduces PyCantonese, an open-source Python library for Cantonese linguistics and natural language processing. After the library design, implementation, corpus data format, and key datasets included are introduced, the paper provides an overview of the currently implemented functionality: stop words, handling Jyutping romanization, word segmentation, part-of-speech tagging, and parsing Cantonese text.

pdf bib A Comprehensive Cantonese Dictionary Dataset with Definitions, Translations and Transliterated Examples
Chaak-ming Lau | Grace Wing-yan Chan | Raymond Ka-wai Tse | Lilian Suet-ying Chan
Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference

This paper discusses the compilation of the Cantonese dictionary dataset, which was compiled through manual annotation over a period of 7 years. Cantonese is a low-resource language with limited tagged or manually checked resources, especially at the sentential level, and this dataset is an attempt to fill the gap. The dataset contains over 53,000 entries of Cantonese words, which comes with basic lexical information (Jyutping phonemic transcription, part-of-speech tags, usage tags), manually crafted definitions in Written Cantonese, English translations, and Cantonese examples with English translation and Jyutping transliterations. Special attention has been paid to handle character variants, so that unintended “character errors” (equivalent to typos in phonemic writing systems) are filtered out, and intra-speaker variants are handled. Fine details on word segmentation, character variant handling, definition crafting will be discussed. The dataset can be used in a wide range of natural language processing tasks, such as word segmentation, construction of semantic web and training of models for Cantonese transliteration.