mlsanityData to 14 Oct 2026

Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model

arXiv 2307.14785 ↗First posted 27 Jul 2023cs.CLcited by 5

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Abstract (arXiv)

This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new state-of-the-art results on the Czech ABSA.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 27 Jul 2023.

Builds on

24 of 51 references.

References · 24

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12 more
  1. 2018Jointly Predicting Predicates and Arguments in Neural Semantic Role Labelingcited by 34
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  3. 2021Czert -- Czech BERT-like Model for Language Representationcited by 22
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  6. 2021Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Argumentscited by 14
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.