|Table of Contents|

Multi-label Text Classification Based on Seq2Seq Modeland Deep Topic Feature Extraction(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年04期
Page:
61-68
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Multi-label Text Classification Based on Seq2Seq Modeland Deep Topic Feature Extraction
Author(s):
Chen Wenshi; Liu Xinhui; Lu Mingyu
School of Information Science and Technology,Dalian Maritime University,Dalian 116026,China
Keywords:
multi-label text classification; deep topic feature extraction; label correlation; seq2seq; attention mechanism
PACS:
TP311
DOI:
10.3969/j.issn.1001-4616.2019.04.009
Abstract:
In this paper,a model based on seq2seq model and deep topic feature extraction is proposed to realize multi-label text classification. Aiming at the problem of feature semantics loss in traditional multi-label text classification method,a model is proposed to extract the local features of texts by using the Long Short-term Memory(LSTM)network and extract the global features of texts by using topic model(Latent Dirichlet Allocation,LDA)named Deep Topic Feature Extraction Model(DTFEM),and then obtain the semantic coding vector with deep semantic feature,and the vector is used as the input of the decoder network. The decoder network regards the task of multi-label text classification as the process of sequence generation,solves the problem of label correlation of multi-label text classification,and adds the attention mechanism to calculate the probability distribution of attention,highlights the effect of key input on the output,improves the semantic missing problem due to excessive input,and realizes the final multi-label text classification. The experimental results show that the model can obtain better results than the traditional multi-label text classification system. In addition,the experiments have shown that the use of deep topic features can improve the performance of multi-label text classification.

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Last Update: 2019-12-31