번역 준비 지수: 논문-특허 연관성 측정
이 논문은 특허와 연관된 연구를 식별하기 위해 논문의 텍스트를 기반으로 하는 Translation Readiness Index (TRI)를 제안합니다. SPECTER2를 사용하여 논문의 제목과 초록에서 768차원의 semantic embeddings를 추출하고, XGBoost 모델을 통해 patent-paper-paired class에 속할 확률을 계산합니다. 분석 결과, TRI 점수가 높은 논문은 발명 중심의 framing을 사용하는 경향이 있으며, 실제 외부 지표와도 양의 상관관계를 보였습니다.
arXiv:2606.31102v1 Announce Type: new Abstract: Universities, funders, investors, and policy agencies often need to identify research with translational relevance before patents, licenses, startups, or industry collaborations are visible. This study introduces the Translation Readiness Index (TRI), a text-based measure evaluating a publication's semantic similarity to papers that appear in high-confidence patent-paper pairs. Using 20,610 publications from OpenAlex, including 9,431 publications from the Reliance on Science patent-paper pairs data and 11,179 matched comparison publications, we created paper-level 768-dimensional semantic embeddings from titles and abstracts with SPECTER2. After evaluating four machine learning classifiers, XGBoost achieved the highest ROC-AUC (0.77). We define TRI as the model-estimated probability that a publication belongs to the patent-paper-paired class. Linguistic analysis revealed that patent-paired publications more often use an invention-oriented framing, distinct from the observational language of the comparison group. External validation across University of Western Australia (UWA) publications and leading global universities demonstrated positive associations between high TRI scores and independent translational indicators. TRI provides a text-based method for identifying translation-ready research, though it should be interpreted as a measure of semantic proximity to patented science rather than a direct measure of realized commercialization.