针对马拉地语长距离实体识别，评估BERT模型并提出适配策略。
Long Range Named Entity Recognition for Marathi Documents
- 基于BERT的Transformer模型处理马拉地语长距离实体识别。
- 对比英文学界与马拉地语文本的识别效果差异。
- 为低资源语言NLP提供可复用的适配方法，适合多语言研究者。
随着马拉地语数字内容的指数级增长，对高级自然语言处理（NLP）技术，尤其是命名实体识别（NER）的需求显著上升。尤其在识别长距离实体、整理和理解非结构化马拉地语文本方面，NER至关重要。本文系统分析了当前适用于马拉地语文档的NER技术，重点探讨了BERT Transformer模型在长距离马拉地语NER中的潜力。通过对比已有方法的效果，论文进一步比较了英语文献中的NER实践，并提出了适用于马拉地语文本的适配策略。研究还讨论了马拉地语特有的语言特征和语境细微差别带来的挑战，强调了NER在NLP中的关键作用。该工作标志着提升马拉地语NER技术的重要进展，具有广泛应用于各类NLP任务和领域的潜力。
原文摘要 · Abstract (English)
The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language digital content. In particular, NER is essential for recognizing distant entities and for arranging and understanding unstructured Marathi text data. With an emphasis on managing long-range entities, this paper offers a comprehensive analysis of current NER techniques designed for Marathi documents. It dives into current practices and investigates the BERT transformer model's potential for long-range Marathi NER. Along with analyzing the effectiveness of earlier methods, the report draws comparisons between NER in English literature and suggests adaptation strategies for Marathi literature. The paper discusses the difficulties caused by Marathi's particular linguistic traits and contextual subtleties while acknowledging NER's critical role in NLP. To conclude, this project is a major step forward in improving Marathi NER techniques, with potential wider applications across a range of NLP tasks and domains.
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