自然语言文本知识结构的智能提取

I. Kuznetsov, E. Kozerenko, A. Matskevich
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引用次数: 4

摘要

研究了一种从自然语言文本中提取对象及其链接的语义语言处理器。它适用于需要自动形式化自然语言文本流的领域。文本的特殊性被处理器的语言知识考虑在内:系统可以调整到不同的主题领域。我们描述了在不同的主题领域使用该处理器进行文本形式化,例如犯罪学(事件摘要、指控结论等)、大众媒体(关于恐怖活动的文件)、人事管理(自传、简历)。每个问题领域的特殊特征被检查:提取对象的集合,识别它们的方法,它们的连接,发生的缩写,标点符号和特殊符号,语言结构的特定特征,等等——所有这些特殊特征都被考虑到语言知识的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Extraction of Knowledge Structures from Natural Language Texts
A semantic linguistic processor which extracts the objects and their links from natural language texts is considered. It is intended for the areas where the automatic formalization of the flows of texts in natural language is required. Peculiarities of the texts are taken into account by linguistic knowledge of the processor: the system can be tuned to various subject areas. We describe the use of this processor for text formalization in different subject areas, such as criminology (summary of incidents, accusatory conclusions, etc.), mass media (documents about terrorist activities), personnel management (autobiographies, resume). Special features of each problem area are examined: the collections of extracted objects, the means for their identification, their connections, occurring contractions, punctuation and special signs, specific character of language constructions, etc. -- all these special features were taken into account in the linguistic knowledge development.
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