Version 1
: Received: 17 December 2023 / Approved: 18 December 2023 / Online: 18 December 2023 (08:29:51 CET)
How to cite:
Frank, J.; Patel, R.; Terry, C. Aspect-Targeted Opinion Word Extraction with Aspect Subword Segmentation. Preprints2023, 2023121280. https://doi.org/10.20944/preprints202312.1280.v1
Frank, J.; Patel, R.; Terry, C. Aspect-Targeted Opinion Word Extraction with Aspect Subword Segmentation. Preprints 2023, 2023121280. https://doi.org/10.20944/preprints202312.1280.v1
Frank, J.; Patel, R.; Terry, C. Aspect-Targeted Opinion Word Extraction with Aspect Subword Segmentation. Preprints2023, 2023121280. https://doi.org/10.20944/preprints202312.1280.v1
APA Style
Frank, J., Patel, R., & Terry, C. (2023). Aspect-Targeted Opinion Word Extraction with Aspect Subword Segmentation. Preprints. https://doi.org/10.20944/preprints202312.1280.v1
Chicago/Turabian Style
Frank, J., Rodolfo Patel and Cleva Terry. 2023 "Aspect-Targeted Opinion Word Extraction with Aspect Subword Segmentation" Preprints. https://doi.org/10.20944/preprints202312.1280.v1
Abstract
Contemporary advanced models for aspect-targeted opinion word extraction (ATOWE), which predominantly utilize BERT-based encoders at a word level, have shown limited advancements when integrated with graph convolutional networks (GCNs) for syntactic tree assimilation. Recognizing the prowess of BERT subwords in encapsulating rare or context-poor words, this study pivots from syntactic trees to BERT subwords, omitting GCNs from the structural framework. Our approach, named Aspect-Enhanced Wordpiece Extraction Model (AEWEM), focuses on refining aspect representation during encoding. We propose an input format of paired sentence-aspect, diverging from traditional single-sentence inputs. AEWEM demonstrates superior performance on benchmark datasets, establishing a robust foundation for future explorations in this domain.
Keywords
Sentiment Analysis; Opinion Words Extraction; Syntax Feature
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
