Technology identification from patent texts: a novel named entity recognition method

Identifying technologies is a key element for mapping a domain and its evolution. It allows managers and decision makers to anticipate trends for an accurate forecast and effective foresight. Researchers and practitioners are taking advantage of the rapid growth of the publicly accessible sources to map technological domains. Among these sources, patents are the widest technical open access database used in the literature and in practice. Nowadays, Natural Language Processing (NLP) techniques enable new methods for the analysis of patent texts. Among these techniques, in this paper we explore the use of Named Entity Recognition (NER) with the purpose to identify the technologies mentioned in patents' text. We compare three different NER methods, gazetteer-based, rule-based and deep learning-based (e.g. BERT), measuring their performances in terms of precision, recall and computational time. We test the approaches on 1600 patents from four assorted IPC classes as case studies. Our NER systems collected over 4500 fine-grained technologies, achieving the best results thanks to the combination of the three methodologies. The proposed method overcomes the literature thanks to the ability to filter generic technological terms. Our study delineates a valid technology identification tool that can be integrated in any text analysis pipeline to support academics and companies in investigating a technological domain.