Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection
This paper describes a system built in the SemEval-2022 competition. As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks i…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment Analysis+1PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning
This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection. Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities th…
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3
This paper presents a combination of data augmentation methods to boost the performance of state-of-the-art transformer-based language models for Patronizing and Condescending Language (PCL) detection and multi-label PCL…
Data AugmentationFew-Shot LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion
This paper describes our system used in the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI). Multimedia automatic misogyny recognition consists of the identification of misogynous memes, taking a…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification
Women are frequently targeted online with hate speech and misogyny using tweets, memes, and other forms of communication. This paper describes our system for Task 5 of SemEval-2022: Multimedia Automatic Misogyny Identifi…
Binary ClassificationMeme ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification
In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to class…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtext-classification+1I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques
Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has be…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSarcasm DetectionUMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic
In this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic documents. Our team achieve in …
Binary ClassificationEnsemble LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores
Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amount…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTowards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
We are concerned by Data Driven Requirements Engineering, and in particular the consideration of user's reviews. These online reviews are a rich source of information for extracting new needs and improvement requests. In…
ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+10/1 Deep Neural Networks via Block Coordinate Descent
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity a…
10-shot image generation16k2D Object Detection+92All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)
This paper considers the problem of Hierarchical Multi-Label Classification (HMC), where (i) several labels can be present for each example, and (ii) labels are related via a domain-specific hierarchy tree. Guided by the…
AllClassificationHierarchical Multi-label ClassificationMulti-Label Classification+1SwinCheX: Multi-label classification on chest X-ray images with transformers
According to the considerable growth in the avail of chest X-ray images in diagnosing various diseases, as well as gathering extensive datasets, having an automated diagnosis procedure using deep neural networks has occu…
BenchmarkingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLarge Loss Matters in Weakly Supervised Multi-Label Classification
Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost. In t…
ClassificationMemorizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOnline Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap-aware speaker diarization by formulatin…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONspeaker-diarizationSpeaker DiarizationSemantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Prob…
Hierarchical Multi-label ClassificationLogical ReasoningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRED v2: Enhancing RED Dataset for Multi-Label Emotion Detection
RED (Romanian Emotion Dataset) is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts, containing single-label annotated tweets with one of the following emotions: joy, f…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONregressionThe Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification
This paper addresses the semi-automatic annotation of subjects, also called policy areas, in the Danish Parliament Corpus (2009-2017) v.2. Recently, the corpus has been made available through the CLARIN-DK repository, th…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONInvestigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these dataset…
Active LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer
Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cancer (NSCLC). Histol…
Few-Shot LearningLung Cancer DiagnosisMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1