research-article

Social Media Text Generation Based on Neural Network Model

Authors:

Jiarun Cao,

Chongwen WangAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 58 - 61

https://doi.org/10.1145/3297156.3297206

Published: 08 December 2018 Publication History

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Abstract

The social media text is increasing rapidly in recent years. With this background, natural language generation technology is mature enough for implementing an NLG system in some general field, but the circumstance is difficult in the social media field because of the linguistic arbitrariness. This paper presents a neural network model building a social media NLG system. Compared with state-of-art model, our system outperforms the existing NLG system in the social media field.

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Cited By

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Li BYang PSun YHu ZYi M(2024)Advances and challenges in artificial intelligence text generation人工智能文本生成的进展与挑战Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230041025:1(64-83)Online publication date: 8-Feb-2024
https://doi.org/10.1631/FITEE.2300410
Yang ZJiang HDeng ALi Y(2024)Topic-Oriented Controlled Text Generation for Social NetworksJournal of Signal Processing Systems10.1007/s11265-023-01907-296:2(131-151)Online publication date: 12-Feb-2024
https://doi.org/10.1007/s11265-023-01907-2
Fatima NImran AKastrati ZDaudpota SSoomro A(2022)A Systematic Literature Review on Text Generation Using Deep Neural Network ModelsIEEE Access10.1109/ACCESS.2022.317410810(53490-53503)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3174108
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Index Terms

Social Media Text Generation Based on Neural Network Model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

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 ACM 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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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2018

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CSAI '18

CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

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Cited By

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Li BYang PSun YHu ZYi M(2024)Advances and challenges in artificial intelligence text generation人工智能文本生成的进展与挑战Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230041025:1(64-83)Online publication date: 8-Feb-2024
https://doi.org/10.1631/FITEE.2300410
Yang ZJiang HDeng ALi Y(2024)Topic-Oriented Controlled Text Generation for Social NetworksJournal of Signal Processing Systems10.1007/s11265-023-01907-296:2(131-151)Online publication date: 12-Feb-2024
https://doi.org/10.1007/s11265-023-01907-2
Fatima NImran AKastrati ZDaudpota SSoomro A(2022)A Systematic Literature Review on Text Generation Using Deep Neural Network ModelsIEEE Access10.1109/ACCESS.2022.317410810(53490-53503)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3174108
Yang Y(2021)BiEAF: An Bidirectional Enhanced Attention Flow Model for Question Answering Task2021 2nd International Conference on Information Science and Education (ICISE-IE)10.1109/ICISE-IE53922.2021.00086(344-348)Online publication date: Nov-2021
https://doi.org/10.1109/ICISE-IE53922.2021.00086
Liu YYu P(2021)Point2Token: A Multi-Tagging Answer Retrieval Framework for Question Answering2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)10.1109/ICAA53760.2021.00069(339-343)Online publication date: Jun-2021
https://doi.org/10.1109/ICAA53760.2021.00069
Liu YHao B(2021)Intra-and-inter Sentence Attention Model for Enhanced Question Answering System2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)10.1109/ICAA53760.2021.00029(115-121)Online publication date: Jun-2021
https://doi.org/10.1109/ICAA53760.2021.00029
Li R(2021)An End-to-end Question Answering Model Based on Semantic-enhancing Attention Mechanism2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)10.1109/AINIT54228.2021.00072(333-337)Online publication date: Oct-2021
https://doi.org/10.1109/AINIT54228.2021.00072
Cao SYue Y(2019)Financial News Generation Based on Artificial Intelligence Technology2019 2nd International Conference on Safety Produce Informatization (IICSPI)10.1109/IICSPI48186.2019.9095962(398-401)Online publication date: Nov-2019
https://doi.org/10.1109/IICSPI48186.2019.9095962

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