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11 September 2023 Coreference disambiguation based on two-layer label dependence analysis
Hongfei Xu, Yingna Li
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Proceedings Volume 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023); 127792A (2023) https://doi.org/10.1117/12.2688903
Event: Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 2023, Kunming, China
Abstract
A two-layer labeling-based dependency analysis model is proposed to introduce the location features of words for the coreference disambiguation of entities. Firstly, two layers of labels are used for labeling, the second layer labels the location information of words, the features of sentences are learned using a bidirectional long short-term memory network, the dependency syntax analysis based on deep graph decoding obtains the dependency tree of sentences, and the two layers of labels are fused to improve the performance and accuracy of coreference disambiguation. Experiments are conducted on the text dataset of power security entity relationship extraction, and the results show that the two-layer labeled dependency analysis has an improved effect on co-finger disambiguation, which verifies the effectiveness of the model for co-finger disambiguation on the experimental dataset.
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Hongfei Xu and Yingna Li "Coreference disambiguation based on two-layer label dependence analysis", Proc. SPIE 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 127792A (11 September 2023); https://doi.org/10.1117/12.2688903
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KEYWORDS
Machine learning
Data modeling
Deep learning
Semantics
Neural networks
Process modeling
Mathematical optimization
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