Kazuyuki Motohashi


Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here


2024

pdf bib
From Technology to Market. Bilingual Corpus on the Evaluation of Technology Opportunity Discovery
Amir Hazem | Kazuyuki Motohashi | Chen Zhu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

As companies aim to enhance and expand their product portfolios, Technology Opportunity Discovery (TOD) has gained increasing interest. To comprehend the role of emerging technologies in innovation, we introduce a novel technology-market corpus in English and Japanese languages, and conduct a comprehensive empirical evaluation of the linkage between technology and the market. Our dataset comprises English patents extracted from the USPTO database and Japanese patents from the Japanese Patent Office (JPO), along with their associated products for each stock market company. We compare several static and contextualized word embedding methods to construct a technology-market space and propose an effective methodology based on a fine-tuned BERT model for linking technology to the market.