  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.
