TEI editions are among the most used tool by scholarly editors to produce digital editions in various literary fields. LIFT is a Python-based tool that allows to programmatically extract information from digital texts annotated in TEI by modelling persons, places, events and relations annotated in the form of a Knowledge Graph which reuses ontologies and controlled vocabularies from the Digital Humanities domain.
Every scholar in digital humanities and/or social sciences has probably already faced the challenge posed by consulting large digital newspaper archives in order to extract detailed information about a topic. It is beyond any doubt that computational-oriented methods and tools currently available may provide a great contribution; however, applying such methods and tools could pose several difficulties, especially in dealing with large ensembles of items.
Introduction by OpenMethods Editor (Erzsébet Tóth-Czifra): Research on date extractions from literature brings us closer to answering big questions of “when literature takes place”. As Frank Fischer’s blog post, First of May in German literature shows, beyond mere quantification, this line of research also yields insights on the cultural significance of certain dates. In this case, the significance of 1st of May in German literature (as reflected in the “Corpus of German-Language Fiction” dataset) was determined with the help of a freely accessible data set and the open access tool HeidelTime. The brief description of the workflow is a smart demonstration of the potential of open DH methods and data sharing in sustainable ways.
Bonus one: the post starts out from briefly touching upon some of Frank’s public humanities activities.
Bonus two: mention of the Tiwoli (“Today in World Literature”) app, a fun side product built on to pof the date extraction research.
In the next episode, we are looking behind the scenes of two ontologies: NeMO and the Scholarly Ontology (SO) with Panos Constantopoulos and Vayianos Pertsas who tell us the story behind these ontologies and explain how they can be used to ease or upcycle your daily works as a researcher. We discuss the value of knowledge graphs, how NeMO and SO connect with the emerging DH ontology landscape and beyond, why Open Access is a precondition of populating them, the Greek DH landscape …and many more!
OpenMethods Spotlights showcase people and epistemic reflections behind Digital Humanities tools and methods. You can find here brief interviews with the creator(s) of the blogs or tools that are highlighted on OpenMethods to humanize and contextualize them. In the first episode, Alíz Horváth is talking with Hilde de Weerdt at Leiden University about MARKUS, a tool that offers offers a variety of functionalities for the markup, analysis, export, linking, and visualization of texts in multiple languages, with a special focus on Chinese and now Korean as well.
East Asian studies are still largely underrepresented in digital humanities. Part of the reason for this phenomenon is the relative lack of tools and methods which could be used smoothly with non-Latin scripts. MARKUS, developed by Brent Ho within the framework of the Communication and Empire: Chinese Empires in Comparative Perspective project led by Hilde de Weerdt at Leiden University, is a comprehensive tool which helps mitigate this issue. Selected as a runner up in the category “Best tool or suite of tools” in the DH2016 awards, MARKUS offers a variety of functionalities for the markup, analysis, export, linking, and visualization of texts in multiple languages, with a special focus on Chinese and now Korean as well.
Introduction: GROBID is an already well-known open source tool in the field of Digital Humanities, originally built to extract and parse bibliographical metadata from scholarly works. The acronym stands for GeneRation Of BIbliographic Data.
Shaped by use cases and adoptions to a range of different DH and non-DH settings, the tool has been progressively evolved into a suite of technical features currently applied to various fields, like that of journals, dictionaries and archives.
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Introduction: In this blog post, James Harry Morris introduces the method of web scraping. Step by step from the installation of the packages, readers are explained how they can extract relevant data from websites using only the Python programming language and convert it into a plain text file. Each step is presented transparently and comprehensibly, so that this article is a prime example of OpenMethods and gives readers the equipment they need to work with huge amounts of data that would no longer be possible manually.
Introduction: The indispensable Programming Historian comes with an introduction to Term Frequency – Inverse Document Frequency (tf-idf) provided by Matthew J. Lavin. The procedure, concerned with specificity of terms in a document, has its origins in information retrieval, but can be applied as an exploratory tool, finding textual similarity, or as a pre-processing tool for machine learning. It is therefore not only useful for textual scholars, but also for historians working with large collections of text.
Introduction: Studying n-grams of characters is today a classical choice in authorship attribution. If some discussion about the optimal length of these n-grams have been made, we have still have few clues about which specific type of n-grams are the most helpful in the process of efficiently identifying the author of a text. This paper partly fills that gap, by showing that most of the information gained from studying n-grams of characters comes from the affixes and punctuation.