Joint Embedding of Words and Category Labels for Hierarchical Multi-label Text Classification

Autor:	Zhao, Jingpeng, Ma, Yinglong
Rok vydání:	2020
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Computation and Language Computer Science - Machine Learning I.2.7
Druh dokumentu:	Working Paper
Popis:	Text classification has become increasingly challenging due to the continuous refinement of classification label granularity and the expansion of classification label scale. To address that, some research has been applied onto strategies that exploit the hierarchical structure in problems with a large number of categories. At present, hierarchical text classification (HTC) has received extensive attention and has broad application prospects. Making full use of the relationship between parent category and child category in text classification task can greatly improve the performance of classification. In this paper, We propose a joint embedding of text and parent category based on hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC. Our method makes full use of the connection between the upper-level and lower-level labels. Experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost. Comment: The submitted paper (Identifier: arXiv:2004.02555) has a problem of authorship disputes within a collaboration of authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.02555 Zobrazit plný text záznamu View this record from Arxiv