Papers Multi-Label Text Classification

Does Head Label Help for Long-Tailed Multi-Label Text Classification

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e…

Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi

Recently the NLP community has started showing interest towards the challenging task of Hostile Post Detection. This paper present our system for Shared Task at Constraint2021 on "Hostile Post Detection in Hindi". The da…

Hostility Detection and Covid-19 Fake News Detection in Social Media

Withtheadventofsocialmedia,therehasbeenanextremely rapid increase in the content shared online. Consequently, the propagation of fake news and hostile messages on social media platforms has also skyrocketed. In this pape…

Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is know…

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing method…

GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yie…

HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification

The data imbalance problem is a crucial issue for the multi-label text classification. Some existing works tackle it by proposing imbalanced loss objectives instead of the vanilla cross-entropy loss, but their performanc…

Comparison of Machine Learning Methods for Multi-label Classification of Nursing Education and Licensure Exam Questions

In this paper, we evaluate several machine learning methods for multi-label classification of text questions. Every nursing student in the United States must pass the National Council Licensure Examination (NCLEX) to beg…

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation

Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to impr…

Large Scale Legal Text Classification Using Transformer Models

Large multi-label text classification is a challenging Natural Language Processing (NLP) problem that is concerned with text classification for datasets with thousands of labels. We tackle this problem in the legal domai…

Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique

Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content. The most obvious advantage of tag recommendation is the correct classification of information. In this s…

An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels

Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications and presents interesting challenges. First, not all labels are well represented in the training set, d…

Data-Efficient Pretraining via Contrastive Self-Supervision

For natural language processing `text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data. Transfer learning from high-resource p…

Imbalanced Chinese Multi-label Text Classification Based on Alternating Attention

Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data

For natural language processing (NLP) ‘text-to-text’ tasks, prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources. However, this methodology is being critiqued fo…

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the desi…

Label-Wise Document Pre-Training for Multi-Label Text Classification

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge by developing Label-Wise Pre-Training (LW…

I-AID: Identifying Actionable Information from Disaster-related Tweets

Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fin…

Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters 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 f…

Hierarchy-Aware Global Model for Hierarchical Text Classification

Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a…