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

arXiv: https://arxiv.org/abs/1906.07591 · HTML: https://mlsanity.com/paper/1906.07591/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Natural Language Processing in the Legal Domain](https://mlsanity.com/paper/2302.12039/) (2023)
- [FullRecall: A Semantic Search-Based Ranking Approach for Maximizing Recall in Patent Retrieval](https://mlsanity.com/paper/2507.14946/) (2025)

## Abstract (arXiv)

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.