Text similarity semantic calculation based on deep reinforcement learning
by Guanlin Chen; Xiaolong Shi; Moke Chen; Liang Zhou
International Journal of Security and Networks (IJSN), Vol. 15, No. 1, 2020

Abstract: Semantic analysis is a fundamental technology in natural language processing. Semantic similarity calculations are involved in many applications of natural language processing, such as QA system, machine translation, text similarity calculation, text classification, information extraction and even speed recognition, etc. This paper proposes a new framework for computing semantic similarity: deep reinforcement learning for Siamese attention structure model (DRSASM). The model learns word segmentation automatically and word distillation automatically through reinforcement learning. The overall architecture LSTM network to extract semantic features, and then introduces a new attention mechanism model to enhance semantics. The experiment show that this new model on the SNLI dataset and Chinese business dataset can improve the accuracy compared to current base line structure models.

Online publication date: Thu, 09-Apr-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Security and Networks (IJSN):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com