刘世界 (Liú Shì Jiè)

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I am Shijie Liu, a computational linguistic researcher specializing in data-driven approaches to language processing and modeling. My research adopts a computational perspective to examine language phenomena, utilizing data-driven methods and machine learning techniques to address linguistic challenges in specialized domains.

Currently, I am affiliated with both the Maritime Language Data Research Team at Shanghai Maritime University under Prof. Yan Zhang’s supervision, and the Language and Translation Technology Team (LT³) at Ghent University, collaborating with Prof. Véronique Hoste and Prof. Els Lefever. I also completed a joint doctoral training program at Xi’an International Studies University (2023-2024) with Prof. Libo Huang as my co-supervisor. Through these positions, I lead the Top-notch Innovative Talents Cultivation Program on automatic terminology extraction using deep learning approaches, and contribute to the China National Social Science Fund project investigating maritime language standardization through computational methods.

My core research interests encompass computational linguistics with emphasis on data-driven language modeling and analysis, computational terminology focusing on multilingual automatic term extraction, and translation technology application. My recent work extensively explores large language models for terminology extraction, employing zero-shot and few-shot learning paradigms alongside fine-tuning methodologies to enhance extraction performance. Additionally, I have constructed large-scale annotated datasets for maritime safety terminology, developing practical solutions for cross-lingual terminology extraction and domain-specific language applications.

You can find my CV here: Shijie’s CV

Check my latest news HERE !

Feel free to contact me via: Email / GitHub / bilibili

Funny stuff

Try this! I’ve used Google NotebookLM to generate a “mini-pod cast” based on my CV. Technology is amazing! Please note, this is AI-generated, so there might contain certain mistakes and exagerations. Just for FUN! You can listen to it below:

For more info

This personal website is based on Academic Pages, which can be found in the guide, the growing wiki, and you can always ask a question on GitHub. The guides for the Minimal Mistakes theme (which this theme was forked from) might also be helpful.