Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
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Abstract (arXiv)
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.
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References · 5
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2017SPINE: SParse Interpretable Neural Embeddingscited by 55
- 2016Empath: Understanding Topic Signals in Large-Scale Textcited by 51
- 2016Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corporacited by 19
- 2017On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysiscited by 15
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