Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification
R Wang, X Dai - Proceedings of the 60th Annual Meeting of the …, 2022 - aclanthology.org
Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …
natural language processing. Previous studies mainly focus on learning text representation …
Revisiting transformer-based models for long document classification
The recent literature in text classification is biased towards short text sequences (eg,
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
Hierarchy-aware label semantics matching network for hierarchical text classification
Hierarchical text classification is an important yet challenging task due to the complex
structure of the label hierarchy. Existing methods ignore the semantic relationship between …
structure of the label hierarchy. Existing methods ignore the semantic relationship between …
ML-LJP: multi-law aware legal judgment prediction
Legal judgment prediction (LJP) is a significant task in legal intelligence, which aims to
assist the judges and determine the judgment result based on the case's fact description …
assist the judges and determine the judgment result based on the case's fact description …
Label-specific dual graph neural network for multi-label text classification
Multi-label text classification is one of the fundamental tasks in natural language processing.
Previous studies have difficulties to distinguish similar labels well because they learn the …
Previous studies have difficulties to distinguish similar labels well because they learn the …
Label-specific feature augmentation for long-tailed multi-label text classification
Multi-label text classification (MLTC) involves tagging a document with its most relevant
subset of labels from a label set. In real applications, labels usually follow a long-tailed …
subset of labels from a label set. In real applications, labels usually follow a long-tailed …
Rethinking self-attention: Towards interpretability in neural parsing
Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …
to remain explainable. Self-attention is currently widely used, however interpretability is …
A novel reasoning mechanism for multi-label text classification
The aim in multi-label text classification is to assign a set of labels to a given document.
Previous classifier-chain and sequence-to-sequence models have been shown to have a …
Previous classifier-chain and sequence-to-sequence models have been shown to have a …
Tailor versatile multi-modal learning for multi-label emotion recognition
Abstract Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various
human emotions from heterogeneous visual, audio and text modalities. Previous methods …
human emotions from heterogeneous visual, audio and text modalities. Previous methods …
CNN-BiLSTM-Attention: A multi-label neural classifier for short texts with a small set of labels
G Lu, Y Liu, J Wang, H Wu - Information Processing & Management, 2023 - Elsevier
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 …
posted by users on government web portals, so that a message can be directed to one or …