Academic Resource Text Level Multi-label Classification based on Attention

TL;DR

This paper introduces an attention-based hierarchical multi-label classification method for academic texts that effectively integrates text, keywords, and hierarchical labels to improve classification accuracy.

Contribution

The paper presents a novel AHMCA algorithm that combines hierarchical attention with embeddings from word2vec and BiLSTM for academic text classification.

Findings

Effective classification accuracy demonstrated on academic datasets.

Hierarchical attention captures label and keyword associations.

Improved performance over baseline models.

Abstract

Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system. We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure, the academic documents are classified into the most relevant categories. We utilize word2vec and BiLSTM to obtain embedding and latent vector representations of text, keywords, and hierarchies. We use hierarchical attention mechanism to capture the associations between keywords, label hierarchies, and text word vectors to generate hierarchical-specific document embedding vectors to replace the original text embeddings in HMCN-F. The experimental results on the academic text dataset demonstrate the effectiveness of the AHMCA algorithm.