Abstract
The selection of industry partners for Research and Development (R&D) is a challenging task for many organizations. Present methods for partner-selection, based on patents, publications or company databases, do often fail for highly specialized SMEs. Our approach aims at calculating the technological similarity for partner discovery. We apply methods from Natural Language Processing (NLP) on companies’ website texts. We show that the deep-learning language model BERT outperforms other methods at this task. Tested against expert-proven ground truth, it achieves an F1-score up to 0.90. Our results imply that website texts are useful for the purpose of estimating the similarity between companies. We see great potential in the scalability of the semantic analysis of company website texts.
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