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- W4328005926 abstract "During communication operation and maintenance, event co-reference disambiguation can discover the association between events with event similarity calculation, facilitating troubleshooting and recovery. Traditionally characters or simple semantic matching is used to calculate event similarity. It is difficult to reflect the influence of different features of events, resulting in low accuracy of disambiguation. To solve this problems, we propose an event co-reference disambiguation model with the enhanced semantics of multi-layer attention in the communication field. Our model includes a single event feature encoder, event encoder, and event similarity calculation. The key contribution is that we use word attention to reflect the importance of different words in event features. Furthermore, we highlight the importance of different features at different levels by exploiting feature attention. Finally, the proposed model yields significant performance compared with other popular methods on the network fault data in the communication field provided by Huawei." @default.
- W4328005926 created "2023-03-22" @default.
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- W4328005926 date "2022-08-01" @default.
- W4328005926 modified "2023-10-18" @default.
- W4328005926 title "Event Co-reference Disambiguation with Enhanced Semantics of Multi-layer Attention" @default.
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- W4328005926 doi "https://doi.org/10.1109/bigcom57025.2022.00039" @default.
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