首页|Studies in the Area of Machine Learning Reported from Chinese Academy of Agricul tural Sciences (Methodology for Assessing the Influence of Technical Topics Base d on PhraseLDA-SNA and Machine Learning)
Studies in the Area of Machine Learning Reported from Chinese Academy of Agricul tural Sciences (Methodology for Assessing the Influence of Technical Topics Base d on PhraseLDA-SNA and Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from the Chi nese Academy of Agricultural Sciences by NewsRx correspondents, research stated, “[Purpose/Significance] Accurately measur ing the influence of technical topics is crucial for decision-makers to understa nd the developmental trends in the technology sector. It is also an important li nk in identifying emerging, cutting-edge, and disruptive technical topics.” The news journalists obtained a quote from the research from Chinese Academy of Agricultural Sciences: “Traditional methods of measuring technical topic influen ce are significantly affected by the latency of patent data approval and citatio ns, lack a forward-looking perspective on the potential influence of technical t opics, and suffer from insufficient semantic richness in the extraction of techn ical topics. This paper presents a method for measuring technical topic influenc e based on PhraseLDA-SNA and machine learning. It aims to mitigate the impact of delays in patent data approval and citation, while improving the interpretabili ty and accuracy of the results in assessing technical topic influence. [Method/Process] In this study the explicit and implicit deter minants of technical topic influence were first analyzed, based on which an inde x system for measuring technical topic influence was constructed. Then, the Phra seLDA model was used to extract semantically rich technical topics from a large corpus of pre-processed patent texts and to compute the topic-patent association probabilities. PhraseLDA-SNA enhances the semantic richness of technical topic extraction and deepens the analysis of topic content. Machine learning methods l everage their robust data processing and analysis capabilities to predict the hi gh citation potential of patents related to the topics. This research integrates PhraseLDA-SNA and machine learning methods to accurately measure the significan ce and advanced nature of technical topics in promoting field development,there by achieving an accurate measurement of the influence of technical topics. Final ly, an empirical study was conducted in the field of cellulose biodegradation to compare the high-impact technical topics identified by the proposed method with those identified by the traditional method. Several experts with high academic influence and extensive experience in cellulose biodegradation research were inv ited to evaluate the high-impact technical topics identified in this study, thus validating the effectiveness of the proposed method. [Result s/Conclusions] Compared with the traditional method, the tech nical topic influence measurement approach based on PhraseLDA-SNA and machine le arning reveals more in-depth content.”
Chinese Academy of Agricultural SciencesCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
