Introduction of a novel word embedding approach based on technology labels extracted from patent data
This addresses the problem of finding synonyms in patent searches for researchers and professionals, but it appears incremental as it builds on existing embedding methods with a specific data source.
The paper tackles the challenge of diverse patent language by introducing a word embedding approach based on statistical analysis of human-labeled patent data to generate accurate, language-independent vectors for technical terms, with initial qualitative results presented.
Diversity in patent language is growing and makes finding synonyms for conducting patent searches more and more challenging. In addition to that, most approaches for dealing with diverse patent language are based on manual search and human intuition. In this paper, a word embedding approach using statistical analysis of human labeled data to produce accurate and language independent word vectors for technical terms is introduced. This paper focuses on the explanation of the idea behind the statistical analysis and shows first qualitative results. The resulting algorithm is a development of the former EQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.