Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features

TL;DR

This paper introduces a novel keyword extraction method based on syntactic features and NLP to improve patent retrieval accuracy by better capturing claim relevance compared to traditional techniques.

Contribution

It presents a new approach combining syntactic analysis and NLP for extracting keywords from patent claims, enhancing prior art search effectiveness.

Findings

Keyword extraction outperforms tf-idf in search results

Syntactic features improve relevance of retrieved patents

Method yields better search performance on patent claims

Abstract

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.