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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-06-12
摘要
Word embedding models are a powerful approach for representing the multidimensional conceptual spaces within which communicated concepts relate, combine, and compete with one another. This class of models represent a recent advance in 機器學習 allowing scholars to efficiently encode 複雜系統s of meaning with minimal semantic distortion based on local and global word co-occurrences from large-scale text data. Although their use has the potential to broaden theoretical possibilities within organization science, embeddings are largely unknown to organizational scholars, where known they have only been mobilized for a narrow set of uses, and they remain unlinked to a theoretical scaffolding that can enable cumulative theory building within the organizations community. Our goal is to demonstrate the pr
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