@inproceedings{70da9591656c4acb8bf0eb9271249fa7,
title = "Recommendation of Small-Sample Indicator Based on Sentence-BERT",
abstract = "The recommendation of system capability indicators can provide a basis for combat effectiveness evaluation and improve the efficiency of indicator data collection, but the existing traditional methods are too subjective and inefficient. The article proposes an intelligent recommendation method for system capability indicators based on semantic understanding technology: firstly, crawling open-source weakly related semantic matching training sets, publicly available military articles and other relevant textual data, applying large language models to construct model training datasets suitable for the military domain; secondly, establishing a Chinese semantic matching model based on Sentence-BERT to achieve similarity scoring and ranking of input indicators and other texts; finally, designing simulation experiments to verify the feasibility and accuracy of this method, which can provide reliable support and reference for relevant decision-making.",
keywords = "Indicator Recommendation, LLM, semantic matching, Sentence-BERT",
author = "Zenglin Li and Yujie Cui and Xinyu Zhang and Wenfeng Wu and Bo Li",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2252-8_39",
language = "英语",
isbn = "9789819622511",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "388--398",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control",
}