AMR-based Network for Aspect-based Sentiment Analysis

Ma, Fukun; Hu, Xuming; Liu, Aiwei; Yang, Yawen; Li, Shuang; Yu, Philip S.; Wen, Lijie

doi:10.18653/v1/2023.acl-long.19

Cited by 5 publications

(2 citation statements)

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“…Lastly, the performance gain of RAPL on cross-domain tasks is lower than that on in-domain tasks. An intriguing avenue for future research is to explore techniques for better performance on cross-domain tasks, e.g., data augmentation (Hu et al, 2023c) and structured knowledge guidance (Liu et al, 2022a;Ma et al, 2023a).…”

Section: Limitationsmentioning

confidence: 99%

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

Meng,

Hu,

Liu

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot documentlevel relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. Metric-based meta-learning is an effective framework widely adopted for FSDLRE, which constructs class prototypes for classification. However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype. 2) They use a set of generic NOTA (none-of-the-above) prototypes across all tasks, neglecting that the NOTA semantics differs in tasks with different target relation types. In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. By judiciously leveraging the relation descriptions and realistic NOTA instances as guidance, our method effectively refines the relation prototypes and generates task-specific NOTA prototypes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by average 2.61% F 1 across various settings of two FSDLRE benchmarks. 1

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Section: Limitationsmentioning

confidence: 99%

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

Meng,

Hu,

Liu

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…In the last decade, deep neural networks have achieved remarkable success in many fields, including computer vision , natural language processing (Ma et al 2023), etc. As for the spatiotemporal prediction tasks, existing methods can be grouped into two types.…”

Section: Introductionmentioning

confidence: 99%

iTrendRNN: An Interpretable Trend-Aware RNN for Meteorological Spatiotemporal Prediction

Huang,

Luo,

Zhang

et al. 2024

AAAI

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Accurate prediction of meteorological elements, such as temperature and relative humidity, is important to human livelihood, early warning of extreme weather, and urban governance. Recently, neural network-based methods have shown impressive performance in this field. However, most of them are overcomplicated and impenetrable. In this paper, we propose a straightforward and interpretable differential framework, where the key lies in explicitly estimating the evolutionary trends. Specifically, three types of trends are exploited. (1) The proximity trend simply uses the most recent changes. It works well for approximately linear evolution. (2) The sequential trend explores the global information, aiming to capture the nonlinear dynamics. Here, we develop an attention-based trend unit to help memorize long-term features. (3) The flow trend is motivated by the nature of evolution, i.e., the heat or substance flows from one region to another. Here, we design a flow-aware attention unit. It can reflect the interactions via performing spatial attention over flow maps. Finally, we develop a trend fusion module to adaptively fuse the above three trends. Extensive experiments on two datasets demonstrate the effectiveness of our method.

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Enhancing aspect-based sentiment analysis with BERT-driven context generation and quality filtering

Zhao,

Feng,

Sun

et al. 2024

Natural Language Processing Journal

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AMR-based Network for Aspect-based Sentiment Analysis

Cited by 5 publications

References 0 publications

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

iTrendRNN: An Interpretable Trend-Aware RNN for Meteorological Spatiotemporal Prediction

Enhancing aspect-based sentiment analysis with BERT-driven context generation and quality filtering

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