基于句法特征提取关键词的专利检索查询生成
Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features
摘要：
本文介绍了一种从专利权利要求中提取相关关键词的新方法，作为检索具有类似权利要求的其他专利（检索现有技术）任务的一部分。该方法结合了定性分析的写作风格的权利要求与自然语言处理方法来解析文本，以表示一个法律文本作为一个专业化的术语树状结构。在这种情况下，提取的关键字集合产生的搜索结果比使用传统方法（如tf-idf）提取的关键字更好。性能是在由提取的关键字组成的查询的搜索结果上测量的。
英文摘要：
This paper describes a new method to extract relevant keywords from patent claims, as part of the task of retrieving other patents with similar claims (search for prior art). The method combines a qualitative analysis of the writing style of the claims with NLP methods to parse text, in order to represent a legal text as a specialization arborescence of terms. In this setting, the set of extracted keywords are yielding better search results than keywords extracted with traditional methods such as tf-idf. The performance is measured on the search results of a query consisting of the extracted keywords.
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。
