Deep Learning for Period Classification of Historical Texts

In this study, we address the interesting task of classifying historical texts by their assumed period of writing. This task is useful in digital humanity studies where many texts have unidentified publication dates. For years, the typical approach for temporal text classification was supervised using machine-learning algorithms. These algorithms require careful feature engineering and considerable domain expertise to design a feature extractor to transform the raw text into a feature vector from which the classifier could learn to classify any unseen valid input. Recently, deep learning has produced extremely promising results for various tasks in natural language processing (NLP). The primary advantage of deep learning is that human engineers did not design the feature layers, but the features were extrapolated from data with a general-purpose learning procedure. We investigated deep learning models for period classification of historical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN), and recurrent neural networks (RNN). We demonstrate that the CNN and RNN models outperformed the paragraph vector model and supervised machine-learning algorithms. In addition, we constructed word embeddings for each time period and analyzed semantic changes of word meanings over time

Research in the Archival Multiverse

Over the past 15 years, the field of archival studies around the world has experienced unprecedented growth within the academy and within the profession, and archival studies graduate education programs today have among the highest enrolments in any information field. During the same period, there has also been unparalleled expansion and innovation in the diversity of methods and theories being applied in archival scholarship. Global in scope, Research in the Archival Multiverse compiles critical and reflective essays across a wide range of emerging research areas and interests in archival studies; it aims to provide current and future archival academics with a text addressing possible methods and theoretical frameworks that have been and might be used in archival scholarship and research

Research in the Archival Multiverse

Over the past 15 years, the field of archival studies around the world has experienced unprecedented growth within the academy and within the profession, and archival studies graduate education programs today have among the highest enrolments in any information field. During the same period, there has also been unparalleled expansion and innovation in the diversity of methods and theories being applied in archival scholarship. Global in scope, Research in the Archival Multiverse compiles critical and reflective essays across a wide range of emerging research areas and interests in archival studies; it aims to provide current and future archival academics with a text addressing possible methods and theoretical frameworks that have been and might be used in archival scholarship and research