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Soft Computing
Published in: Soft Computing 11/2020

12-11-2019 | Focus

A deep extraction model for an unseen keyphrase detection

Authors: Amin Ghazi Zahedi, Morteza Zahedi, Mansoor Fateh

Published in: Soft Computing | Issue 11/2020

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Abstract

The keyphrase represents the basic concepts for a text. In many natural language processing tasks, it is necessary to extract qualitative keyphrases. Considering previous studies regarding text modeling, the meanings and concepts associated with the text had not been particularly considered as significant. According to recent research, cluster-related documents have a good subscription, especially in the keyphrases that are not directly appearing in a text document. Therefore, in this study, the main structure of the proposed model is based on the keyphrases disappearing in the document. We called it unseen keyphrase. Considering the proposed method, a model is developed to extract the basic concepts of the text using the same text estimates and through adding keyphrases to the deep network hidden layers of training. The main purpose of this structure is to first make visible unseen keyphrase and then to use an RNN to predict them. Considering the proposed method, the problem of not representing basic concepts and the unseen keyphrase are significantly solved. This study provides new insight into the concept of text. This mechanism is used by highlighting the role of unseen keyphrase that appears directly without the need for external knowledge. This method is tested on four public datasets in this field. The results revealed an average improvement of 12% compared to the public methods such as TF-IDF, KEA, and RNN.
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Metadata
Title
A deep extraction model for an unseen keyphrase detection
Authors
Amin Ghazi Zahedi
Morteza Zahedi
Mansoor Fateh
Publication date
12-11-2019
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 11/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04486-2

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