Named Entity Recognition Approaches and Their Comparison for Custom NER Model
Named Entity Recognition Approaches and Their Comparison for Custom NER Model
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命名实体识别方法及其自定义 NER 模型的比较
DOI：
10.1080/0194262x.2020.1759479
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发表时间：
2020
影响因子：
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通讯作者：
Poorva Agrawal
中科院分区：
文献类型：
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作者：
Hemlata Shelar;Gagandeep Kaur;Neha Heda;Poorva Agrawal
摘要 命名实体识别（NER）是一种自然语言处理工具，用于从非结构化文本数据（例如电子邮件、报纸、博客等）中提取信息。NER 是识别文本、句子或段落字符串中提到的名词（如人物、地点、组织等）的过程。为了构建 NER 系统，可以使用许多使用 Java、Python 和 Cython 语言的不同库和自然语言处理工具。所有这些工具都具有预训练的 NER 模型，可以导入、使用并可以根据要求进行修改或定制。本文介绍了不同的 NLP 库，包括 Python 的 SpaCy、Apache OpenNLP 和 TensorFlow。其中一些库提供了可以定制的预构建 NER 模型。这些库的比较是根据训练准确性、F 分数、预测时间、模型大小和训练难易程度进行的。所有模型的训练和测试数据都是相同的。当考虑所有模型的整体性能时，Python 的 Spacy 给出了更高的精度和最好的结果。
ABSTRACT Named entity recognition (NER) is a natural language processing tool for information extraction from unstructured text data such as e-mails, newspapers, blogs, etc. NER is the process of identifying nouns like people, place, organization, etc., that are mentioned in the string of the text, sentence, or paragraph. For building the NER system, many different libraries and natural language processing tools using Java, Python, and Cython languages are available. All these tools have pretrained NER models that can be imported, used and can be modified or customized according to requirements. This paper explains different NLP libraries including Python’s SpaCy, Apache OpenNLP, and TensorFlow. Some of these libraries provide a pre-build NER model that can be customized. The comparison of these libraries is done based on training accuracy, F-score, prediction time, model size, and ease of training. The training and testing data are the same for all the models. When considering the overall performance of all the models, Python’s Spacy gives a higher accuracy and the best result.