对比主流模型在多语言地理实体识别中的表现，助力全球安全应用。
Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection
- 用英文、俄文、阿拉伯文的Telegram数据测试模型
- GPT-4在多语言地理实体识别中表现最佳，准确率超90%
- 适合关注跨语言地理信息分析的研究者与安全领域应用者
先进自然语言处理方法与大语言模型的融合显著提升了从多语言文本中提取和分析地理空间数据的能力，对国家安全等领域能产生重要影响。本文全面评估了SpaCy、XLM-RoBERTa、mLUKE、GeoLM等主流NLP模型以及OpenAI的GPT 3.5和GPT 4等大语言模型在多语言地理实体识别任务中的表现。基于来自英文、俄文和阿拉伯文Telegram频道的数据集，通过准确率、精确率、召回率和F1分数等指标进行评测，揭示各模型在不同语言环境下的优势与局限。研究结果表明，模型在跨语言地理实体识别中仍面临显著挑战，但为未来更高效、包容性强的NLP工具开发提供了方向，推动地理空间分析技术在国际安全等领域的应用。
原文摘要 · Abstract (English)
The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these models through metrics such as accuracy, precision, recall, and F1 scores, to assess their effectiveness in accurately identifying geospatial references. The analysis exposes each model's distinct advantages and challenges, underscoring the complexities involved in achieving precise geo-entity identification across varied linguistic landscapes. The conclusions drawn from this experiment aim to direct the enhancement and creation of more advanced and inclusive NLP tools, thus advancing the field of geospatial analysis and its application to global security.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。