Papers Multi-Label Classification

A no-regret generalization of hierarchical softmax to extreme multi-label classification

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled b…

Node Representation Learning for Directed Graphs

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue th…

HierLPR: Decision making in hierarchical multi-label classification with local precision rates

In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure. HierLPR is motivated by a new metr…

Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification

The digitalization of the legal domain has been ongoing for a couple of years. In that process, the application of different machine learning (ML) techniques is crucial. Tasks such as the classification of legal document…

A Context-aware Capsule Network for Multi-label Classification

Recently proposed Capsule Network is a brain inspired architecture that brings a new paradigm to deep learning by modelling input domain variations through vector based representations. Despite being a seminal contributi…

Multi-Task Learning as Multi-Objective Optimization

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off.…

Learning Discriminators as Energy Networks in Adversarial Learning

We propose a novel framework for structured prediction via adversarial learning. Existing adversarial learning methods involve two separate networks, i.e., the structured prediction models and the discriminative models, …

HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization

We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages…

Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence …

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-…

Content Explorer: Recommending Novel Entities for a Document Writer

Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further resea…

以深層類神經網路標記中文階層式多標籤語意概念 (Hierarchical Multi-Label Chinese Word Semantic Labeling using Deep Neural Network ) [In Chinese]

Extremely Randomized CNets for Multi-label Classification

Multi-label classification (MLC) is a challenging task in ma-chine learning consisting in the prediction of multiple labels associated with a single instance. Promising approaches for MLC are those able to capt…

A Novel Online Stacked Ensemble for Multi-Label Stream Classification

As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each ins…

Understanding Behavior of Clinical Models under Domain Shifts

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive mode…

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classificati…

Multi-label Classification of User Reactions in Online News

The increase in the number of Internet users and the strong interaction brought by Web 2.0 made the Opinion Mining an important task in the area of natural language processing. Although several methods are capable of per…

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding r…

Deep Generative Models for Weakly-Supervised Multi-Label Classification

In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data. Since such annotation process is in general costly, we focus o…

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music based on its reviews on websites. The b…