面向机器学习的引文数据集与引文实体提取
Citation Data-set for Machine Learning Citation Styles and Entity Extraction from Citation Strings
摘要：
引文分析是学术界搜索引擎和知识产权保护的基础。在评价文献的相似性和计算其引文影响力时，还需要进一步提取元数据。引文解析涉及到引文字符串的识别和解剖到他们的书目组成部分，如“作者”，“卷”，“标题”等，这种元数据可能很难准确地获取，由于成千上万的不同风格和噪音，可以应用到书目创建引文字符串。已经存在许多方法来完成引文字符串的准确解析。本文描述了一个大的数据集，可以用来帮助这些方法的训练，这些方法有有限的数据。它还描述了调查，如果这些方法的垮台引用解析，特别是基于机器学习的方法是因为缺乏相关的数据大小用于训练他们。
英文摘要：
Citation parsing is fundamental for search engines within academia and the protection of intellectual property. Meticulous extraction is further needed when evaluating the similarity of documents and calculating their citation impact. Citation parsing involves the identification and dissection of citation strings into their bibliography components such as "Author", "Volume","Title", etc. This meta-data can be difficult to acquire accurately due to the thousands of different styles and noise that can be applied to a bibliography to create the citation string. Many approaches exist already to accomplish accurate parsing of citation strings. This dissertation describes the creation of a large data-set which can be used to aid in the training of these approaches which have limited data.It also describes the investigation into if the downfall of these approaches to citation parsing and in particular the machine learning based approaches is because of the lack of size associated to the data used to train them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
