Hierarchical Multi-label Classification of Text with Capsule Networks

Aly, Rami; Remus, Steffen; Biemann, Chris

doi:10.18653/v1/p19-2045

Cited by 74 publications

(45 citation statements)

References 19 publications

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“…Another group of algorithms called hierarchical multi-label classification methods has been proposed for leveraging the hierarchical relationships among labels in making predictions, which has been successfully exploited for text processing [36], visual recognition [37, 38] and genomic analysis [39]. One common approach is to train classifiers on conditional data with all parent-level labels being positive and then to finetune them with the whole dataset [12], which contains both the positive and negative samples.…”

Section: Problem Formulationmentioning

confidence: 99%

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Pham¹,

Le²,

Tran³

et al. 2019

Preprint

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Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem by training state-of-the-art CNNs that exploit dependencies among abnormality labels. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. This is the highest AUC score yet reported to date. The proposed method is also evaluated on the independent test set of the CheXpert competition, which is composed of 500 CXR studies annotated by a panel of 5 experienced radiologists. The performance is on average better than 2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of writing this paper.

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Section: Problem Formulationmentioning

confidence: 99%

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Pham¹,

Le²,

Tran³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In our study, we employ three evaluation metrics to measure the performances of different approaches to multi-modal multi-label emotion detection, i.e., multi-label Accuracy (Acc), Hamming Loss (HL) and micro F 1 measure (F 1 ). These metrics have been popularly used in some multi-label classification problems (Li et al, 2015;Aly et al, 2019;Wu et al, 2019).…”

Section: Experimental Settingsmentioning

confidence: 99%

Multi-modal Multi-label Emotion Detection with Modality and Label Dependence

Zhang¹,

Ju²,

Li³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.

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“…Intent detection task aims to classify the intent of queries and is always considered as a text classification task (Kim, 2014;Lai et al, 2015;Yang et al, 2016;Joulin et al, 2017;Xia et al, 2018). Considering the complexity of the label, some hierarchical text classification methods (Huang et al, 2019;Mao et al, 2019;Aly et al, 2019) have emerged to capture label hierarchies. Recently there are some joint models to jointly learn the intent detection and…”

Section: Intent Detectionmentioning

confidence: 99%

Joint Intent Detection and Entity Linking on Spatial Domain Queries

Wang

Zhou

Yu³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants. In this paper, we first study the LU problem in the spatial domain, which is a critical problem for providing location-based services by voice assistants, but is without in-depth investigation in existing studies. Spatial domain queries have several unique properties making them be more challenging for language understanding than common conversational queries, including lexicalsimilar but diverse intents and highly ambiguous words. Thus, a special tailored LU framework for spatial domain queries is necessary. To the end, a dataset SMQ was extracted and annotated based on the real-life queries from a voice assistant service. We then proposed a new multi-task framework MELIP that jointly learns the intent detection and entity linking tasks on the SMQ with invented hierarchical intent detection method and triple-scoring mechanism for entity linking. A specially designed spatial GCN is also utilized to model spatial context information among entities. We have conducted extensive experimental evaluations with state-of-the-art entity linking and intent detection methods, which demonstrated that MELIP can outperform all baselines with a significant margin.

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Hierarchical Multi-label Classification of Text with Capsule Networks

Cited by 74 publications

References 19 publications

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Multi-modal Multi-label Emotion Detection with Modality and Label Dependence

Joint Intent Detection and Entity Linking on Spatial Domain Queries

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