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Deep Learning Approach for Extracting Catch Phrases from Legal Documents

Deep Learning Approach for Extracting Catch Phrases from Legal Documents

Kayalvizhi S., Thenmozhi D.
Copyright: © 2020 |Pages: 16
ISBN13: 9781799811596|ISBN10: 179981159X|ISBN13 Softcover: 9781799811602|EISBN13: 9781799811619
DOI: 10.4018/978-1-7998-1159-6.ch009
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MLA

S., Kayalvizhi, and Thenmozhi D. "Deep Learning Approach for Extracting Catch Phrases from Legal Documents." Neural Networks for Natural Language Processing, edited by Sumathi S. and Janani M., IGI Global, 2020, pp. 143-158. https://doi.org/10.4018/978-1-7998-1159-6.ch009

APA

S., K. & D., T. (2020). Deep Learning Approach for Extracting Catch Phrases from Legal Documents. In S. S. & J. M. (Eds.), Neural Networks for Natural Language Processing (pp. 143-158). IGI Global. https://doi.org/10.4018/978-1-7998-1159-6.ch009

Chicago

S., Kayalvizhi, and Thenmozhi D. "Deep Learning Approach for Extracting Catch Phrases from Legal Documents." In Neural Networks for Natural Language Processing, edited by Sumathi S. and Janani M., 143-158. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1159-6.ch009

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Abstract

Catch phrases are the important phrases that precisely explain the document. They represent the context of the whole document. They can also be used to retrieve relevant prior cases by the judges and lawyers for assuring justice in the domain of law. Currently, catch phrases are extracted using statistical methods, machine learning techniques, and deep learning techniques. The authors propose a sequence to sequence (Seq2Seq) deep neural network to extract catch phrases from legal documents. They have employed several layers, namely embedding layer, encoder-decoder layer, projection layer, and loss layer to build the deep neural network. The methodology is evaluated on IRLeD@FIRE-2017 dataset and the method has obtained 0.787 and 0.607 as mean average precision and recall scores respectively. Results show that the proposed method outperforms the existing systems.

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