Scholarlink

This chapter provides an in-depth exploration of named entity recognition (NER) and keyword extraction technologies. It begins with definitions and challenges of NER and keyword extraction, progressing to an analysis of key techniques such as rule-based, machine learning, and deep learning methods. The chapter highlights advancements in models like BiLSTM-CRF and IDCNN-CRF, emphasizing their efficiency in sequence labeling tasks. Additionally, keyword extraction methods are introduced, including statistical, graph-based, and embedding approaches such as term frequency-inverse document frequency (TF-IDF) and TextRank. Finally, practical examples are presented, including the NLPIR-ICTCLAS-DocExtractor, showing its performance in real-world entity extraction tasks.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Named Entity and Keyword Extraction

  • 章节

  • 发表日期：

    2025年

查看全文
  • Huaping Zhang,
  • Jianyun Shang

摘要

This chapter provides an in-depth exploration of named entity recognition (NER) and keyword extraction technologies. It begins with definitions and challenges of NER and keyword extraction, progressing to an analysis of key techniques such as rule-based, machine learning, and deep learning methods. The chapter highlights advancements in models like BiLSTM-CRF and IDCNN-CRF, emphasizing their efficiency in sequence labeling tasks. Additionally, keyword extraction methods are introduced, including statistical, graph-based, and embedding approaches such as term frequency-inverse document frequency (TF-IDF) and TextRank. Finally, practical examples are presented, including the NLPIR-ICTCLAS-DocExtractor, showing its performance in real-world entity extraction tasks.