Papers Multi-Label Text Classification
“Multi-Label Text Classification” 태그가 달린 논문 181편 · 필터 해제
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, t…
Language ModelingLanguage ModellingLarge Language ModelMath+1Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains. Most existing approaches require an enormous amount of annotated data to learn…
Language ModelingLanguage ModellingMulti Label Text ClassificationMulti-Label Text Classification+3Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
Prompt Tuning is emerging as a scalable and cost-effective method to fine-tune Pretrained Language Models (PLMs), which are often referred to as Large Language Models (LLMs). This study benchmarks the performance and com…
BenchmarkingClassificationComputational EfficiencyMulti-Label Classification+5MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
The eXtreme Multi-label text Classification(XMC) refers to training a classifier that assigns a text sample with relevant labels from an extremely large-scale label set (e.g., millions of labels). We propose MatchXML, an…
Multi Label Text ClassificationMulti-Label Text ClassificationSentenceSentence Embeddings+2Recent Advances in Hierarchical Multi-label Text Classification: A Survey
Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical. It is a vital task in many real world applications, e.g. scientif…
ClassificationMulti Label Text ClassificationMulti-Label Text ClassificationSurvey+2DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce
Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated resear…
Multi Label Text ClassificationMulti-Label Text ClassificationRepresentation Learningtext-classification+1Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification
The study of human values is essential in both practical and theoretical domains. With the development of computational linguistics, the creation of large-scale datasets has made it possible to automatically recognize hu…
Contrastive LearningMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces
In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance,…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with …
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Text Classification+2Retrieval-augmented Multi-label Text Classification
Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this problem through retrieval augmentation, aimi…
ClassificationMulti Label Text ClassificationMulti-Label Text ClassificationRetrieval+2F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks
Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABE…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we co…
ClassificationDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems
The USA Food and Drug Administration has accorded increasing importance to patient-reported problems in clinical and research settings. In this paper, we explore one of the largest online datasets comprising 170,141 open…
Multilabel Text ClassificationMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification
Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation…
Multi Label Text ClassificationMulti-Label Text ClassificationPredictionSentence+2Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy
Extreme multi-label text classification utilizes the label hierarchy to partition extreme labels into multiple label groups, turning the task into simple multi-group multi-label classification tasks. Current research enc…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning…
Language ModellingMulti Label Text ClassificationMulti-Label Text ClassificationTAG+2CinPatent: Datasets for Patent Classification
Patent classification is the task that assigns each input patent into several codes (classes). Due to its high demand, several datasets and methods have been introduced. However, the lack of both systematic performance c…
ClassificationMulti Label Text ClassificationMulti-Label Text ClassificationPatent classification+6Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models
Background: Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. …
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationHarnessing label semantics to extract higher performance under noisy label for Company to Industry matching
Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assign…
AI AgentMulti Label Text ClassificationMulti-Label Text ClassificationSemantic Similarity+4An Effective Deployment of Contrastive Learning in Multi-label Text Classification
The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge …
ClassificationContrastive LearningMulti-Label ClassificationMulti Label Text Classification+3