University of Twente Student Theses
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How does the integration of syntactic features (POS tags or dependency parsing) during BERT fine-tuning influence the performance of semantic parsing?
Zhang, Jinrui (2025) How does the integration of syntactic features (POS tags or dependency parsing) during BERT fine-tuning influence the performance of semantic parsing?
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| Abstract: | BERT has achieved outstanding performance in many NLP tasks, but its implicit handling of syntax may limit its effectiveness in shallow semantic parsing. This study explores incorporating explicit syntactic features, such as POS tags and dependency parsing labels into BERT. This is completed through two approaches: addition or concatenation either at the input embedding layer or after transformer layers. Experiments have been conducted, and results show that the additive approach with dependency parsing labels at input embedding layer achieves the highest F1 score, improving performance by 1.24%. This work provides insights into the integration of syntactic features in BERT. Additional Key Words and Phrases: BERT, Semantic Parsing, Large Language Model, BIO tagging, Dependency Parsing, POS tagging. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/105088 |
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