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A Semantic Composition Computing Model Integrating Dependency Syntax and Semantic Roles from an Educational Perspective

Published: 08 November 2024 Publication History

Abstract

An effective semantic composition computational model can improve the semantic understanding ability of the system in practical applications such as text mining, information retrieval, AI translation, question answering system, and interdisciplinary research, thereby improving the overall performance. Therefore, it is of great practical significance to study semantic composition computation. The two cores of semantic composition computing are composition composition and composition mode. This paper focuses on the combination method, and proposes a semantic composition computing model that integrates dependency syntax and semantic role, which is introduced from dependency syntax, semantic role and the realization of the fusion of two kinds of information. Finally, the model is trained and evaluated on the paraphrase recognition task, and compared with the representative work. The results show that the accuracy of the model in this paper is significantly improved on the paraphrase recognition task, indicating the effectiveness of the semantic composition computing model in this paper.

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    IoTML '24: Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning
    August 2024
    443 pages
    ISBN:9798400710353
    DOI:10.1145/3697467
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 08 November 2024

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