Curatr：历史文学文本语义分析与策展平台
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
AI总结：
针对数字文学馆藏规模大、内容多样导致的相关内容识别提取难题，研究人员提出搭载机器学习语义搜索的Curatr平台，结合神经词嵌入与领域知识生成主题词库，支持从近代数字化文本大语料库中策展子语料库，服务数字人文研究。
摘要：
历史与当代文学数字馆藏的日益普及为人文学科的新研究提供了大量可能性。然而，此类馆藏的规模庞大且内容多样，在识别和提取相关内容方面带来了特殊挑战。本文提出Curatr——一款面向文学探索与策展的在线平台，搭载机器学习支持的语义搜索功能，是在数字人文学术研究语境下设计的。该平台提供一套文本挖掘工作流，将神经词嵌入与专家领域知识相结合，可生成主题词库，使研究人员能够从包含18、19世纪数字化文本的大型语料库中策展相关子语料库。
英文摘要：
The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities. The scale and diversity of such collections however, presents particular challenges in identifying and extracting relevant content. This paper presents Curatr, an online platform for the exploration and curation of literature with machine learning-supported semantic search, designed within the context of digital humanities scholarship. The platform provides a text mining workflow that combines neural word embeddings with expert domain knowledge to enable the generation of thematic lexicons, allowing researches to curate relevant sub-corpora from a large corpus of 18th and 19th century digitised texts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
