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 …

Binary Relevance is a well-known framework for multi-label classification, which considers
each class label as a binary classification problem. Many existing multi-label algorithms are …

Learning the correlation among labels is a standing-problem in the multi-label image
recognition task. The label correlation is the key to solve the multi-label classification but it is …

Abstract Extreme Multi-label Text Classification (XMTC) is a key task of finding the most
relevant labels from a large label set for a document. Although some deep learning-based …

Multi-label text classification is an increasingly important field as large amounts of text data
are available and extracting relevant information is important in many application contexts …

We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese
posted by users on government web portals, so that a message can be directed to one or …

Recently the deep learning techniques have achieved success in multi-label classification
due to its automatic representation learning ability and the end-to-end learning framework …

Multi-label learning deals with the problem where each instance is associated with a set of
class labels. In multi-label learning, different labels may have their own inherent …

Multi-label text classification has been applied to a multitude of tasks, including document
indexing, tag suggestion, and sentiment classification. However, many of these methods …

Nowadays, multi-label classification methods are of increasing interest in the areas such as
text categorization, image annotation and protein function classification. Due to the …