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31.
鉴于在目前的技术机会识别中存在研判的创新路径往往较为抽象和模糊,并在很多情况下需领域专家参与解读的问题,以冷库技术为例,研究构建基于文本挖掘、机器学习算法及多维空间专利地图的技术创新路径识别模型。首先,构建技术创新路径识别框架对相关专利文献进行分词、清洗等预处理并建立知识图谱;其次,采用融合词频-逆文档频率（TF-IDF）文本挖掘方法对专利文档提取关键词,继而采用隐含狄利克雷分布（LDA）算法对主题聚类降维并萃取创新维度;再次,依据目标技术问题和目标优选创新法则耦合变换于多维空间专利地图并具象出具有现实意义、有价值前景的创新路径;最后,利用可拓学计算各创新路径综合关联度评级优选。以期减少创新成本、提高创新效率,为企业精准开展技术创新、不断提升核心竞争力提供决策参考。 相似文献
32.
《Information processing & management》2023,60(5):103423
The growing use of Artificial Intelligence-enabled recruitment systems has become an important component of modern talent recruitment, particularly through social networks such as LinkedIn and Facebook. However, data overflow embedded in recruitment systems, based on Natural Language Processing (NLP) methods, may result in unconscious gender bias. The purpose of this work is to utilize a set of methods to analyze and detect textual bias in different groups. We analyzed a training dataset of fourteen thousand LinkedIn profiles, which was provided by a company named Talenya, and included job-candidates that fit IT-related positions. We aimed to detect textual self-presentation gender gap patterns, utilizing Term Frequency - Inverse Document Frequency (TF-IDF), word2vec and the Universal Sentence Encoder (USE) to code the data, and applied the kernel two-sample test for the purpose of determining whether men’s and women’s LinkedIn profiles have the same distribution. Additionally, we focused on quantifying and identifying repetition in skills representation in the LinkedIn profile by applying the TF-IDF and cosine similarity tools and compared the repetitiveness pattern of men’s and women’s profiles. Gender-based analysis was also carried out on smaller, more homogeneous groups of candidates, who share the same position type, geographical location and organizational seniority level. Finally, we provide theoretical and practical implications. 相似文献