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.

文献数字化收集的规模和多样性带来了挑战，基于机器学习支持的语义搜索的在线平台 Curatr 提供了一个文本挖掘流程，将神经单词嵌入与领域专家知识相结合，使研究人员能够从大型 18 和 19 世纪数字化文本语料库中筛选出相关的子语料库。

Curatr：一个历史文学文本语义分析和策展平台

Curatr: A Platform for Semantic Analysis and Curation of Historical  Literary Texts

We introduce the Collection Space Navigator (CSN), a browser-based
visualization tool to explore, research, and curate large collections of visual
digital artifacts that are associated with multidimensional data, such as
vector embeddings or tables of metadata. Media objects such as images are often
encoded as numerical vectors, for e.g. based on metadata or using machine
learning to embed image information. Yet, while such procedures are widespread
for a range of applications, it remains a challenge to explore, analyze, and
understand the resulting multidimensional spaces in a more comprehensive
manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve
to project high-dimensional data into low dimensional visualizations, yet
require interpretation themselves as the remaining dimensions are typically
abstract. Here, the Collection Space Navigator provides a customizable
interface that combines two-dimensional projections with a set of configurable
multidimensional filters. As a result, the user is able to view and investigate
collections, by zooming and scaling, by transforming between projections, by
filtering dimensions via range sliders, and advanced text filters. Insights
that are gained during the interaction can be fed back into the original data
via ad hoc exports of filtered metadata and projections. This paper comes with
a functional showcase demo using a large digitized collection of classical
Western art. The Collection Space Navigator is open source. Users can
reconfigure the interface to fit their own data and research needs, including
projections and filter controls. The CSN is ready to serve a broad community.

介绍 Collection Space Navigator（CSN），一个浏览器基于的可视化工具，用于探索、研究和策划大型与多维数据相关的视觉数字文物的集合，如向量嵌入或元数据表。