Papers Multi-Label Text Classification
“Multi-Label Text Classification” 태그가 달린 논문 181편 · 필터 해제
Joint Embedding of Words and Category Labels for Hierarchical Multi-label Text Classification
Text classification has become increasingly challenging due to the continuous refinement of classification label granularity and the expansion of classification label scale. To address that, some research has been applie…
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+2Deep Learning Based Multi-Label Text Classification of UNGA Resolutions
The main goal of this research is to produce a useful software for United Nations (UN), that could help to speed up the process of qualifying the UN documents following the Sustainable Development Goals (SDGs) in order t…
ClassificationDeep LearningGeneral ClassificationMulti Label Text Classification+5Multi-Label Text Classification using Attention-based Graph Neural Network
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relat…
ClassificationGeneral ClassificationGraph AttentionGraph Neural Network+5MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relat…
Document ClassificationGeneral ClassificationGraph AttentionGraph Neural Network+6Multi-label Relation Modeling in Facial Action Units Detection
This paper describes an approach to the facial action units detections. The involved action units (AU) include AU1 (Inner Brow Raiser), AU2 (Outer Brow Raiser), AU4 (Brow Lowerer), AU6 (Cheek Raise), AU12 (Lip Corner Pul…
General ClassificationMulti-Label ClassificationMulti-Label Text ClassificationRelation+1Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set. We propose a novel deep learning method called APLC-XLNet. Our approach…
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationPySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks. SS3 was created to deal with risk detection over text streams and, therefore, not only supports incr…
BIG-bench Machine LearningClassificationDocument ClassificationGeneral Classification+5t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detect…
Anorexia DetectionClassificationDocument ClassificationGeneral Classification+4Label-Specific Document Representation for Multi-Label Text Classification
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. LSAN …
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+3Generalized Zero-shot ICD Coding
The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is in high demand as the manual coding can be labor-intensive and error-prone. It is a multi-la…
ClassificationGeneral ClassificationGeneralized Zero-Shot LearningMulti Label Text Classification+4Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to he…
Abuse DetectionAbusive LanguageHate Speech DetectionMulti Label Text Classification+3Hierarchical Transfer Learning for Multi-label Text Classification
Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classificatio…
Binary ClassificationClassificationGeneral ClassificationMulti Label Text Classification+4Hierarchical Multi-label Classification of Text with Capsule Networks
Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text cl…
AttributeClassificationGeneral ClassificationHierarchical Multi-label Classification+6NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classif…
ClassificationGeneral ClassificationGPUHierarchical Multi-label Classification+7Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep …
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+3Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement s…
General ClassificationMulti-Label Text ClassificationQuestion AnsweringText ClassificationLarge-Scale Multi-Label Text Classification on EU Legislation
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, annotated with ~4.3k EUROVOC labels, which is suitable for LMTC, few-…
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+3Extreme Multi-Label Legal Text Classification: A case study in EU Legislation
We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union's public document database, annotated with…
General ClassificationMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+2Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are on…
General ClassificationMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two millions patents, our approach outperforms the state of the art by an app…
ClassificationGeneral ClassificationMulti-Label Text ClassificationPatent classification