Unsupervised Keyword Extraction from Polish Legal Texts
This work addresses keyword extraction for Polish legal documents, which is an incremental improvement for domain-specific NLP applications.
The authors tackled keyword extraction from Polish legal texts by adapting the RAKE algorithm with an automatic stoplist selection method based on term distribution statistics, achieving performance improvements in an unsupervised setting.
In this work, we present an application of the recently proposed unsupervised keyword extraction algorithm RAKE to a corpus of Polish legal texts from the field of public procurement. RAKE is essentially a language and domain independent method. Its only language-specific input is a stoplist containing a set of non-content words. The performance of the method heavily depends on the choice of such a stoplist, which should be domain adopted. Therefore, we complement RAKE algorithm with an automatic approach to selecting non-content words, which is based on the statistical properties of term distribution.