Duc-Vu Nguyen


NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language
Khoa Thi-Kim Phan | Duc-Vu Nguyen | Ngan Luu-Thuy Nguyen
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

This paper describes our submissions to the EMNLP 2022 shared task on Understanding Figurative Language as part of the Figurative Language Workshop (FigLang 2022). Our systems based on pre-trained language model T5 are divide-and-conquer models which can address both two requirements of the task: 1) classification, and 2) generation. In this paper, we introduce different approaches in which each approach we employ a processing strategy on input model. We also emphasize the influence of the types of figurative language on our systems.


Span Detection for Aspect-Based Sentiment Analysis in Vietnamese
Kim Nguyen Thi Thanh | Sieu Huynh Khai | Phuc Pham Huynh | Luong Phan Luc | Duc-Vu Nguyen | Kiet Nguyen Van
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling
Duc-Vu Nguyen | Linh-Bao Vo | Ngoc-Linh Tran | Kiet Nguyen | Ngan Nguyen
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation


NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector
Duc-Vu Nguyen | Thin Dang | Ngan Nguyen
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.