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Design of agricultural question answering information extraction method based on improved BILSTM algorithm.

Ruipeng Tang1, Jianbu Yang2, Jianxun Tang3

  • 1Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia. 22057874@siswa.um.edu.my.

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Summary

This study introduces the BE-BILSTM algorithm for agricultural data mining, improving information extraction accuracy for better farmer recommendations. The enhanced method outperforms existing algorithms in analyzing agricultural data.

Keywords:
Agricultural information recommendationInformation extractionKnowledge graphNatural language processingQuestion and answer system

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Area of Science:

  • Agricultural Informatics
  • Data Mining
  • Natural Language Processing

Background:

  • The exponential growth of agricultural data necessitates efficient information extraction for data mining and application.
  • Accurate extraction of relevant information is crucial for developing effective agricultural data analysis tools.

Purpose of the Study:

  • To design and evaluate an agricultural question-answering information extraction method using the BE-BILSTM algorithm.
  • To enhance the performance of agricultural information recommendation systems through improved information extraction.

Main Methods:

  • Utilized Python's Scrapy framework for data crawling (soil types, crop diseases, trade) and preprocessing.
  • Employed entity extraction techniques to convert semi-structured agricultural data.
  • Introduced the BERT (Bidirectional Encoder Representations from Transformers) algorithm to enhance the BILSTM (Bidirectional Long Short-Term Memory) algorithm, creating BE-BILSTM.

Main Results:

  • The BE-BILSTM algorithm demonstrated superior information extraction performance compared to BERT-CRF and BILSTM.
  • The proposed method significantly improves the accuracy of agricultural information recommendation systems.
  • The approach effectively captures semantic and contextual relationships within agricultural question-and-answer data.

Conclusions:

  • The BE-BILSTM algorithm offers a more innovative approach to agricultural information recommendation by focusing on information extraction accuracy.
  • Improved understanding of farmer needs and interests through enhanced data analysis leads to more relevant and practical information recommendations.
  • This study contributes to more effective agricultural data mining and application by refining information extraction techniques.