随着互联网的飞速发展，因网络作弊而产生的垃圾页面越来越多，严重影响了搜索引擎的检索效率和用户体验。反作弊已经成为搜索引擎所面临的最重要挑战之一。但目前的反作弊研究大都是基于页面内容或链接特征的，没有一个通用可行的识别方法。本文主要基于作弊目的的分析，给出作弊页面另一种体系的分类，为基于目的的作弊页面识别起到良好的导向作用。
Abstract
Named Entities are important meaningful units in texts. The recognition and analysis of named entities is of great significance in the field of Web information extraction, Web content management and knowledge engineering, etc. The research on named entities includes named entity recognition, disambiguation, coreference resolution, attribute extraction and relation detection, etc. Focusing on named entity recognition, disambiguation and crosslingual coreference resolution, the paper gives a thorough survey on the state of the art of these tasks, including the challenges, methods, evaluations, performances and the problems to be solved. The paper suggests that, the performances of the current systems of named entity recognition, disambiguation and crosslingual coreference resolution are far from the requirement of largescale practical applications. In the view of methods and approaches, named entity recognition, disambiguation and crosslingual conference resolution should be carried beyond the natural language texts and should be investigated directly among the largescale, redundant, heterogeneous, illformed and noisy web pages.
关键词
计算机应用 /
中文信息处理 /
命名实体识别 /
命名实体排歧 /
命名实体跨语言关联
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Key words
computer application /
Chinese information processing /
Web spam, intention analysis, spam pages taxonomy
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参考文献
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基金
国家973重点基础研究资助项目(2004CB318108);国家自然科学基金资助项目(60621062, 60503064, 60736044);国家863高科技资助项目(2006AA01Z141)
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