A few-shot transfer learning approach using text-label embedding with legal attributes for law article prediction

Chen, Yuh‐Shyan; Chiang, Shin-Wei; Wu, Meng-Luen

doi:10.1007/s10489-021-02516-x

Cited by 19 publications

(2 citation statements)

References 16 publications

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“…BERT (Bidirectional Transformer for Language Understanding) [21], and network pruning [22].Nevertheless, the e cacy of supervised deep learning hinges signi cantly upon both the caliber and abundance of annotated samples, thereby posing challenges in real-world scenarios, encompassing: rstly, the arduous and unbounded nature of annotating copious samples, engendering a dearth of annotated samples requisite for deep learning models; secondly, the heightened temporal demands associated with mining data from internet user-generated content. In contrast to conventional deep learning methodologies, human beings possess the capability to glean insights from scant exemplars, swiftly and accurately discerning the categorization of novel instances.In scenarios characterized by a paucity of annotated samples, tackling the classi cation dilemmas inherent in internet user-generated data through deep learning methodologies has emerged as a primary research endeavor.…”

Section: Text Classi Cation Algorithmmentioning

confidence: 99%

Dual-layer Label Perception Algorithm for POI data with long-tailed distribution

TAN,

WANG

2024

Preprint

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Authentic POI datasets often exhibit a long-tailed distribution, wherein instances vary significantly across labels, rendering conventional training methods ill-equipped to handle such real-world scenarios. This paper introduces a Dual-layer Label Perception Algorithm for POI Data, which pioneers the utilization of prevalent two-layer data labels to formulate a label feature cloud, subsequently enhancing classification accuracy by broadening and refining the distribution of label features. To assess the algorithm's efficacy, extensive experimentation is conducted on genuine urban POI datasets, resulting in a notable 5% enhancement in the comprehensive F1-Score, reaching 79.41%. The experimental findings affirm the algorithm model's efficacy in enhancing its detection performance across various advanced text classification algorithms.

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Section: Text Classi Cation Algorithmmentioning

confidence: 99%

Dual-layer Label Perception Algorithm for POI data with long-tailed distribution

TAN,

WANG

2024

Preprint

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“…Compared with the transfer learning method that only uses one source domain, it can increase the chance that transferring relevant knowledge from source domains to target domain and improve learning result. Today, transfer learning has been applied to COVID-19 recognition [ 30 ], law article prediction [ 31 ], the classification of histological images of colorectal cancer [ 32 ], human action recognition [ 33 ], cross-domain recommendations [ 34 ] and EEG signal analysis [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Selection Transfer Learning with Privacy-Preserving

2022

Neural Process Lett

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Transfer learning has ability to create learning task of weakly labeled or unlabeled target domain by using knowledge of source domain to help, which can effectively improve the performance of target learning task. At present, the increased awareness of privacy protection restricts access to data sources and poses new challenges to the development of transfer learning. However, the research on privacy protection in transfer learning is very rare. The existing work mainly uses differential privacy technology and does not consider the distribution difference between data sources, or does not consider the conditional probability distribution of data, which causes negative transfer to harm the effect of algorithm. Therefore, this paper proposes multi-source selection transfer learning algorithm with privacy-preserving MultiSTLP, which is used in scenarios where target domain contains unlabeled data sets with only a small amount of group probability information and multiple source domains with a large number of labeled data sets. Group probability means that the class label of each sample in target data set is unknown, but the probability of each class in a given data group is available, and multiple source domains indicate that there are more than two source domains. The number of data set contains more than two data sets of source domain and one data set of target domain. The algorithm adapts to the marginal probability distribution and conditional probability distribution differences between domains, and can protect the privacy of target data and improve classification accuracy by fusing the idea of multi-source transfer learning and group probability into support vector machine. At the same time, it can select the representative dataset in source domains to improve efficiency relied on speeding up the training process of algorithm. Experimental results on several real datasets show the effectiveness of MultiSTLP, and it also has some advantages compared with the state-of-the-art transfer learning algorithm.

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