Papers with SynGCN
Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis (2025.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. |
| Approach: | They propose a method to enhance long-range dependencies between aspect and opinion words in ABSA by combining attention mechanisms with a syntax-based Graph Convolutional Network and a Mamba-Transformer module. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks (P19-1)
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Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
| Challenge: | Existing word embedding methods utilize sequential context of a word to learn its embeddment, but such methods result in an explosion of the vocabulary size. |
| Approach: | They propose a flexible Graph Convolution based method for learning word embeddings that utilizes the dependency context of a word without increasing the vocabulary size. |
| Outcome: | The proposed model outperforms existing methods on intrinsic and extrinsic tasks and provides an advantage when used with ELMo. |
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |