Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learn…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOpenAI GymQuestion Answering+1Multi-Label Classification Using Link Prediction
Solving classification with graph methods has gained huge popularity in recent years. This is due to the fact that the data can be intuitively modeled with graphs to utilize high level features to aid in solving the clas…
ClassificationGeneral ClassificationLink PredictionMulti-Label Classification+2Deep tree-ensembles for multi-output prediction
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknes…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4A Flexible Class of Dependence-aware Multi-Label Loss Functions
Multi-label classification is the task of assigning a subset of labels to a given query instance. For evaluating such predictions, the set of predicted labels needs to be compared to the ground-truth label set associated…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONComparison 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…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3NLP-PINGAN-TECH @ CL-SciSumm 2020
We focus on systems for TASK1 (TASK 1A and TASK 1B) of CL-SciSumm Shared Task 2020 in this paper. Task 1A is regarded as a binary classification task of sentence pairs. The strategies of domain-specific embedding and spe…
Binary ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+2Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures
So far, multi-label classification algorithms have been evaluated using statistical methods that do not consider the semantics of the considered classes and that fully depend on abstract computations such as Bayesian Rea…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Efficient Robust Optimal Transport with Application to Multi-Label Classification
Optimal transport (OT) is a powerful geometric tool for comparing two distributions and has been employed in various machine learning applications. In this work, we propose a novel OT formulation that takes feature corre…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTAGDocument-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document ass…
Document-level Relation ExtractionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelation+2Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diag…
Deep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCoherent Hierarchical Multi-Label Classification Networks
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMC…
ClassificationGeneral ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+2Learning to Build User-tag Profile in Recommendation System (UTPM)
User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e.g. gender, age, and location) and user behavior information (e.g. browsing and search histor…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsTAGAuxiliary Task Reweighting for Minimum-data Learning
Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to utilize auxiliary tasks to provide additiona…
Domain AdaptationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi…
Document ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Few-shot Learning for Multi-label Intent Detection
In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select …
Few-Shot LearningIntent DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Method for Handling Multi-class Imbalanced Data by Geometry based Information Sampling and Class Prioritized Synthetic Data Generation (GICaPS)
This paper looks into the problem of handling imbalanced data in a multi-label classification problem. The problem is solved by proposing two novel methods that primarily exploit the geometric relationship between the fe…
InformativenessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSynthetic Data GenerationToxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capable of causing harm. Therefore, identifyin…
Hate Speech DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONText ClassificationMulti-label classification of promotions in digital leaflets using textual and visual information
Product descriptions in e-commerce platforms contain detailed and valuable information about retailers assortment. In particular, coding promotions within digital leaflets are of great interest in e-commerce as they capt…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtext-classification+1A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification
Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchica…
ClassificationEntity TypingGeneral ClassificationMulti-class Classification+2An 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…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+5