Huizhi(elly) Liang
Huizhi(elly) Liang has 3 papers in our library, in 2026, most of them on Word sense disambiguation, Language models & pretraining, Sentiment & emotion; often with Tong Wu, Mingyang Chen, Nicolay Rusnachenko.
OpenAlex · h-index 0 · 2 works · 0 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 3 times in our library · ⭐ Paper Stars ranking: #10828
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📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work
🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 workSentiment & emotion · 1 workWord sense disambiguation · 1 work
Works by year
2020s
Papers
- NCL&HKU-NarrSim at SemEval-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity (2026 · 1 citation)
This paper presents the NCL&HKU-NarrSim system for SemEval-2026 Task 4. The task asks which of two candidate stories is narratively closer to an anchor story, judged on deeper structure rather than shared wording. The authors propose one approach per track. - NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression (2026 · 1 citation)
This paper reports a system for SemEval-2026 Task 3 (DimABSA), Track A, Subtask 1. In this subtask, each aspect mentioned in a sentence receives two continuous scores, valence and arousal, on a 1–9 scale, rather than a categorical polarity label. The authors fine-tune XLM-RoBERTa-base on input pairs of the form… - NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating (2026 · 1 citation)
This SemEval-2026 Task 5 system paper tackles graded word sense plausibility. Given a five-sentence English story from the AmbiStory dataset with an ambiguous homonym and a candidate sense, a system must predict how plausible human annotators find that sense on a 1–5 scale. The authors compare three families of…
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