research-article

Research on Entity Relation Extraction Based on BiLSTM-CRF Classical Probability Word Problems

Authors:

Xiangcheng Jia,

Linjing WuAuthors Info & Claims

ICETC '21: Proceedings of the 13th International Conference on Education Technology and Computers

Pages 62 - 68

https://doi.org/10.1145/3498765.3498775

Published: 08 February 2022 Publication History

Abstract

Mathematical automatic problem solving is a very challenging task in the field of artificial intelligence. The key premise of problem-solving automatically is to understand the problem's meaning. For mathematical word problems with rich semantics, varied forms, and difficult to be understood by machines, this study focuses on solving the difficulty of overlapping entity relations recognition and multiple entity relation extractions across sentences, taking the word problem of the classical probability as the research object, an entity relations extraction method based on sequence annotation is proposed. The BiLSRM-CRF model is used to improve the effect of question comprehension. The experimental study found that, compared with the selected combination of different features based on the CRF model alone, the BiLSTM-CRF model can obtain the effect of the approximate CRF model at a superior cost, and improve the recognition effect of a few relations. Meanwhile, the accuracy of the overall problem understanding also gets improved.

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Cited By

Karthica MMeenakshiSundaram D(2023)Advanced Naive Bayes Machine Learning System for Document Similarity CheckingKristu Jayanti Journal of Computational Sciences (KJCS)10.59176/kjcs.v3i1.2315(81-90)Online publication date: 31-Dec-2023
https://doi.org/10.59176/kjcs.v3i1.2315
Jiang YZhang ZHe LGong TDu JYin X(2023)Application of DA-Bi-SRU and Improved RoBERTa Model in Entity Relationship Extraction for High-Speed Train Bogie2023 6th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT60026.2023.00023(89-96)Online publication date: 28-Jul-2023
https://doi.org/10.1109/DSIT60026.2023.00023
Shnaider PChernysheva AKhlopotov MBabayants C(2023)Generation of Course Prerequisites and Learning Outcomes Using Machine Learning MethodsArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-19-8040-4_3(34-46)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-981-19-8040-4_3

Index Terms

Research on Entity Relation Extraction Based on BiLSTM-CRF Classical Probability Word Problems
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ICETC '21: Proceedings of the 13th International Conference on Education Technology and Computers

October 2021

495 pages

ISBN:9781450385114

DOI:10.1145/3498765

Copyright © 2021 ACM.

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Publication History

Published: 08 February 2022

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National Natural Science Foundation of China

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ICETC 2021

ICETC 2021: 2021 13th International Conference on Education Technology and Computers

October 22 - 25, 2021

Wuhan, China

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Cited By

Karthica MMeenakshiSundaram D(2023)Advanced Naive Bayes Machine Learning System for Document Similarity CheckingKristu Jayanti Journal of Computational Sciences (KJCS)10.59176/kjcs.v3i1.2315(81-90)Online publication date: 31-Dec-2023
https://doi.org/10.59176/kjcs.v3i1.2315
Jiang YZhang ZHe LGong TDu JYin X(2023)Application of DA-Bi-SRU and Improved RoBERTa Model in Entity Relationship Extraction for High-Speed Train Bogie2023 6th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT60026.2023.00023(89-96)Online publication date: 28-Jul-2023
https://doi.org/10.1109/DSIT60026.2023.00023
Shnaider PChernysheva AKhlopotov MBabayants C(2023)Generation of Course Prerequisites and Learning Outcomes Using Machine Learning MethodsArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-19-8040-4_3(34-46)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-981-19-8040-4_3

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