用词嵌入对齐跨领域术语，帮学者发现新概念关联。
Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work
- 将不同学科视为语言社区，用无监督方法对齐术语嵌入空间。
- 原型系统在两例研究中成功识别跨领域概念映射关系。
- 适合需要跨学科探索的科研人员，尤其关注概念迁移者。
学者常需查阅本领域之外的文献，但学科专有名词常成为障碍。以往计算工作多通过简化或摘要消除术语；本文提出新思路：保留术语作为通向新概念空间的桥梁。我们将不同学术领域视为使用不同语言的社群，探索如何将无监督跨语言词嵌入对齐技术应用于领域间术语嵌入空间的概念对齐。我们开发了一个基于对齐领域嵌入的跨领域搜索原型系统，并在两个案例研究中进行了测试。研究揭示了该方法在支持概念探索中的潜力与局限，为未来提供计算支持的跨领域信息检索界面提供了设计启示。
原文摘要 · Abstract (English)
Scholars often explore literature outside of their home community of study. This exploration process is frequently hampered by field-specific jargon. Past computational work often focuses on supporting translation work by removing jargon through simplification and summarization; here, we explore a different approach that preserves jargon as useful bridges to new conceptual spaces. Specifically, we cast different scholarly domains as different language-using communities, and explore how to adapt techniques from unsupervised cross-lingual alignment of word embeddings to explore conceptual alignments between domain-specific word embedding spaces.We developed a prototype cross-domain search engine that uses aligned domain-specific embeddings to support conceptual exploration, and tested this prototype in two case studies. We discuss qualitative insights into the promises and pitfalls of this approach to translation work, and suggest design insights for future interfaces that provide computational support for cross-domain information seeking.
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