An Active Transfer Learning Method Combining Uncertainty with Diversity for Chinese Address Resolution

Hu, Yuwei; Zheng, Xueyuan; Zong, Ping

doi:10.1145/3581807.3581902

Cited by 1 publication

(1 citation statement)

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“…Therefore, this research adopts an active learning strategy [25]. Active learning is a method to select the most convergent training model from a large amount of information data according to specific selection rules [28]. It identifies the unlabeled data with the maximum and most valuable information content as a query sample set, enabling fast and accurate matching, and filtering of high-quality, reliable datasets for subsequent annotation work.…”

Section: Iterative Processing Based On Active Learningmentioning

confidence: 99%

Designing of a Geographical Entity Annotation System Using the BiLSTM+CRF+AGG Model

Xu,

Ling,

Wang

2023

Preprint

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When analyzing user geospatial information through deep learning methods, it is typically necessary to annotate existing geospatial data. Currently, manual annotation methods are commonly employed, suffering from issues related to low efficiency and accuracy. This design is based on the TensorFlow deep learning framework and first realizes the BiLSTM+CRF+AGG deep learning model. Among the models, AGG is the aggregation layer, which is introduced to solve the problem of solid particle size equilibrium. Secondly, based on the characteristics of original data and professional data, an automatic labeling algorithm is proposed. The algorithm first preprocesses the acquired original data set and professional data set, The most valuable unlabeled data set which can make the training model converge quickly is selected from the original data set as the target data set. Sort the target data set based on preset rules and set annotation parameters for the sorted target data set. Based on the set annotation parameters, the corpus is synthesized and used as the annotation result. Thirdly, based on the active learning strategy, a manual annotation auxiliary scheme is proposed, and an Excel generation module that is convenient for manual annotation correction is designed and developed to further improve the efficiency and quality of annotation through iterative processing. The combination of the BiLSTM+CRF+AGG deep learning model and high-quality annotation can accurately identify non-standard, incomplete, or even incorrect geographical information entities. This annotation method has found successful practical application within the context of the research project and has been granted an invention patent.

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Section: Iterative Processing Based On Active Learningmentioning

confidence: 99%