Papers Extreme Multi-Label Classification
“Extreme Multi-Label Classification” 태그가 달린 논문 75편 · 필터 해제
Efficient Text Encoders for Labor Market Analysis
Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skil…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current state-of-the-art methods fall into two ca…
Extreme Multi-Label ClassificationMemorizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss
Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Exploring space efficiency in a tree-based linear model for extreme multi-label classification
Extreme multi-label classification (XMC) aims to identify relevant subsets from numerous labels. Among the various approaches for XMC, tree-based linear models are effective due to their superior efficiency and simplicit…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classificatio…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFrom Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically $10^3$ to $10^{12}$ labels), addressing the dynamic natu…
Extreme Multi-Label ClassificationKeyphrase GenerationMissing LabelsMulti-Label Classification+2Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems either empirically optimize expensive LLM-p…
Extreme Multi-Label ClassificationFact CheckingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Learning with Random Circular Vectors
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks~(DNNs) have demo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningUniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have …
Extreme Multi-Label ClassificationGPUMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning label-label correlations in Extreme Multi-label Classification via Label Features
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly fo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+4In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it …
ClassificationExtreme Multi-Label ClassificationIn-Context LearningMulti-Label Classification+2ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on …
Extreme Multi-Label ClassificationIn-Context LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONGeneralized test utilities for long-tail performance in extreme multi-label classification
Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-tail labels, i.e., most labels have very…
Extreme Multi-Label ClassificationMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to the lack of training instances per label a…
Decision MakingEntity TypingExtreme Multi-Label Classificationintent-classification+5Dual-Encoders for Extreme Multi-Label Classification
Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-labe…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Extreme Multi-Label Skill Extraction Training using Large Language Models
Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes. Since such ads are typically formatted in free text, natural l…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMDACE: MIMIC Documents Annotated with Code Evidence
We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in rece…
Document ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsExtreme Classification for Answer Type Prediction in Question Answering
Semantic answer type prediction (SMART) is known to be a useful step towards effective question answering (QA) systems. The SMART task involves predicting the top-$k$ knowledge graph (KG) types for a given natural langua…
ClassificationClusteringExtreme Multi-Label ClassificationMulti-Label Classification+5Adopting 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+5