人物索引挑战：从凌乱的短文本中提取人物
The Person Index Challenge: Extraction of Persons from Messy, Short Texts
摘要：
当人们在文本中提到他们的名字，姓氏和/或中间名时，可能会有很大的变化，他们的名字被使用，他们的名字是如何排列的，以及他们的名字是否被缩写。如果以非常不同的方式连续提到多个人，特别是短文本可以被视为“混乱”。一旦出现歧义名称，可能无法正确推断与人的关联。尽管有这些可能性，在本文中，我们问一个无监督的算法可以从短文本中建立一个人的索引。我们将个人索引定义为一个结构化的表，它可以根据个人的名字对其进行明确的分类。首先，我们给出了一个正式的定义的问题，并描述了一个程序来生成地面实况数据，为未来的评估。为了给这一挑战的第一个解决方案，基线方法被实施。通过使用我们提出的评估策略，我们测试了基线的性能，并提出进一步的改进建议。为了将来的研究，源代码是公开的。
英文摘要：
When persons are mentioned in texts with their first name, last name and/or middle names, there can be a high variation which of their names are used, how their names are ordered and if their names are abbreviated. If multiple persons are mentioned consecutively in very different ways, especially short texts can be perceived as "messy". Once ambiguous names occur, associations to persons may not be inferred correctly. Despite these eventualities, in this paper we ask how well an unsupervised algorithm can build a person index from short texts. We define a person index as a structured table that distinctly catalogs individuals by their names. First, we give a formal definition of the problem and describe a procedure to generate ground truth data for future evaluations. To give a first solution to this challenge, a baseline approach is implemented. By using our proposed evaluation strategy, we test the performance of the baseline and suggest further improvements. For future research the source code is publicly available.
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。
