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- Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence? 一种独立于语言的命名实体识别方法。
- Named Entity Recognition (NER), as the important foundation of Text Processing, has become a key technology of Natural Language Processing (NLP). 命名实体识别是文本信息处理的重要基础,已经逐步成为自然语言处理的一项关键技术。
- This paper presents a Chinese named entity recognition system that integrates the Hidden Markov Model (HMM) and rules which are automatic extracted from the training corpus. 本文实现的中文命名实体识别系统采用了隐马尔可夫模型(Hidden Markov Model,HMM)与自动规则提取相结合的方法。
- CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks, such as Named Entity Recognition, Information Extraction and Text Chunking. 详细说明： 条件随机场用于NLP中命名实体，组块分析。-CRF++ is a simple; customizable; and implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data.
- Intelligent Method for Name Entity Recognition from Biomedical Text 生物医学文本中命名实体识别的智能化方法
- product named entity recognition 产品命名实体识别
- biological named entity recognition 生物命名实体识别
- Chinese named entity recognition 中文命名实体识别
- Spatial named entity recognition 空间命名实体识别
- Chinese named entity recognition (CNER) 中文名实体识别
- Study on Named Entity Recognition for Product Class in the Text Information 在篇章中面向产品类的命名实体识别研究
- Name Entity Recognition 命名实体识别
- A Chinese Named Entity Recognition System Using Statistics-based and Rules-based Method 一个统计与规则相结合的中文命名实体识别系统
- named entity recognition 命名实体识别
- named entity recognition (NER) 专有名词识别
- named entity recognition( NER) 名实体识别
- named entity recognition(NER) 命名实体识别
- Replace the double quotes with the corresponding named entity, ". 用相应的命名实体"替换双引号。
- The result of experiment indicates that the name entity recognize based on maximum entropy in the condition of relatively little corpus can achieve good performance. 实验结果表明,在训练语料集相对较小的情况下,基于最大熵模型的命名实体识别能够获得较为满意的性能。
- Geographical entity recognition of natural languages can enrich the information resource of GISs and improve their capability of representation and understandability. 实现自然语言中地理命名实体识别不仅能够丰富GIS的信息来源，而且能够提升GIS的表达能力和可理解性。
