Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |
Similar Papers
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa (2021.naacl-main)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) is a fine-grained task in sentiment analysis. |
| Approach: | They compare a model with a dependency parser and a tree from a fine-tuned RoBERTa model to find the polarities for aspects in a sentence. |
| Outcome: | The proposed model outperforms the parser-provided tree on six datasets across four languages. |
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task. |
| Approach: | They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features . |
| Outcome: | The proposed method improves on the state-of-the-art in two aspects of the ABSA task. |
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence. |
| Approach: | They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)
Copied to clipboard
| Challenge: | Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect. |
| Approach: | They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference. |
| Outcome: | The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets. |
A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. |
| Approach: | They propose a hybrid approach to Aspect-Based Sentiment Analysis using transfer learning . they exploit the strengths of large language models and traditional syntactic dependencies . |
| Outcome: | The proposed method exploits the strengths of large language models and traditional syntactic dependencies. |
Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)
Copied to clipboard
| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
| Approach: | They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training . |
| Outcome: | The proposed framework achieves state-of-the-art on four ABSA tasks across multiple benchmark datasets. |
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on developing ad hoc models of varying complexity for ABSA subtasks. |
| Approach: | They propose an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on most domains and achieves competitive results on biomedical domain datasets. |