Hierarchical Semantic Labeling with Adaptive Confidence

Davis, Jim; Tong, Liang; Enouen, James; Ilin, Roman

doi:10.1007/978-3-030-33723-0_14

Cited by 8 publications

(19 citation statements)

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Section: Related Workmentioning

confidence: 99%

“…Hierarchical classification typically employs a label hierarchy in the form of a tree [8,6,5,22,1,18] or directed acyclic graph [7,12] that explicitly injects prior knowledge of the label relationships into the model. The relationship between the labels can be either 'IS-A' or not depending on the associated classification problem [1,8,7,6,5,22,18,3,2]. One could either define the label hierarchy manually with domain knowledge [1,12,18] or derive the hierarchy automatically [8,6,5,7,22] from a well established semantic lexical database, such as WordNet [16].…”

Section: Related Workmentioning

confidence: 99%

“…The relationship between the labels can be either 'IS-A' or not depending on the associated classification problem [1,8,7,6,5,22,18,3,2]. One could either define the label hierarchy manually with domain knowledge [1,12,18] or derive the hierarchy automatically [8,6,5,7,22] from a well established semantic lexical database, such as WordNet [16].…”

Section: Related Workmentioning

confidence: 99%

“…A combined model of both flat and top-down hierarchical classifications is proposed to tackle this issue and novelty detection in [14]. The methods proposed by [8,5,6] instead post-process a given flat classifier's terminal predictions by generalizing the predicted terminal labels in a bottom-up fashion through the hierarchy, where the label generalization depends on estimated label posteriors in the hierarchy.…”

Section: Related Workmentioning

confidence: 99%

“…Our proposed method is most related to the bottom-up approaches of [8,5,6] where no hierarchical information is used in training the base classifier. In [8], a search algorithm is proposed to balance the trade-off between the specificity of the predictions and the overall hierarchical accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Bottom-up Hierarchical Classification Using Confusion-based Logit Compression

Liang,

Davis,

Ilin

2021

Preprint

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In this work, we propose a method to efficiently compute label posteriors of a base flat classifier in the presence of few validation examples within a bottom-up hierarchical inference framework. A stand-alone validation set (not used to train the base classifier) is preferred for posterior estimation to avoid overfitting the base classifier, however a small validation set limits the number of features one can effectively use. We propose a simple, yet robust, logit vector compression approach based on generalized logits [6] and label confusions for the task of label posterior estimation within the context of hierarchical classification. Extensive comparative experiments with other compression techniques are provided across multiple sized validation sets, and a comparison with related hierarchical classification approaches is also conducted. The proposed approach mitigates the problem of not having enough validation examples for reliable posterior estimation while maintaining strong hierarchical classification performance.

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