Category: Text

Mining ethnicity: Discourse-driven topic modelling of immigrant discourses in the USA, 1898–1920

Mining ethnicity: Discourse-driven topic modelling of immigrant discourses in the USA, 1898–1920

Introduction: The article illustrates the application of a ‘discourse-driven topic modeling’ (DDTM) to the analysis of the corpus ChronicItaly comprising several newspapers in Italian language, appeared in the USA during the time of massive migration towards America between the end of the XIX century and the first two decades of the XX (1898-1920).

The method combines both Text Modelling (™) and the discourse-historical approach (DHA) in order to get a more comprehensive representation of the ethnocultural and linguistic identity of the Italian group of migrants in the historical American context in crucial periods of time like that immediately preceding the eruption and that of the unfolding of World War I.

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Pipelines for languages: not only Latin! The Italian NLP Tool (Tint)

Pipelines for languages: not only Latin! The Italian NLP Tool (Tint)

The StandforCore NLP wishes to represent a complete Java-based set of tools for various aspects of language analysis, from annotation to dependency parsing, from lemmatization
to coreference resolution. It thus provides a range of tools which
can be potentially applied to other languages apart from English.

Among the languages to which the StandfordCore NLP is mainly applied there is Italian, for which the Tint pipeline has been developed as described in the paper “Italy goes to Stanford: a collection of CoreNLP modules for Italian” by Alessio Palmero Apostolo and Giovanni Moretti.

On the Tint webpage the whole pipeline can be found and downloaded: it comprises tokenization and sentence splitting, morphological analysis and lemmatization, part-of-speech tagging, named-entity recognition and dependency parsing, including wrappers under construction. [Click ‘Read more’ for the whole post.]

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Narrelations — Visualizing Narrative Levels and their Correlations with Temporal Phenomena

Narrelations — Visualizing Narrative Levels and their Correlations with Temporal Phenomena

Introduction: Introduction by OpenMethods Editor (Christopher Nunn): Information visualizations are helpful in detecting patterns in large amounts of text and are often used to illustrate complex relationships. Not only can they show descriptive phenomena that could be revealed in other ways, albeit slower and more laborious, but they can also heuristically generate new knowledge. The authors of this article did just that. The focus here is, fortunately, on narratological approaches that have so far hardly been combined with digital text analyzes, but which are ideally suited for them. To eight German novellas a variety of interactive visualizations were created, all of which show: The combination of digital methods with narratological interest can provide great returns to Literary Studies work. After reading this article, it pays to think ahead in this field.

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Analyzing Documents with TF-IDF | Programming Historian

Analyzing Documents with TF-IDF | Programming Historian

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.

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‘Voyant Tools’

‘Voyant Tools’

Introduction: Digital humanists looking for tools in order to visualize and analyze texts can rely on ‘Voyant Tools’ (https://voyant-tools.org), a software package created by S.Sinclair and G.Rockwell. Online resources are available in order to learn how to use Voyant. In this post, we highlight two of them: “Using Voyant-Tools to Formulate Research Questions for Textual Data” by Filipa Calado (GC Digital Fellows and the tutorial “Investigating texts with Voyant” by Miriam Posner.

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Standardization Survival Kit – Create a dictionary in TEI

Standardization Survival Kit – Create a dictionary in TEI

Introduction: Standards are best explained in real life use cases. The Parthenos Standardization Survival Kit is a collection of research use case scenarios illustrating best practices in Digital Humanities and Heritage research. It is designed to support researchers in selecting and using the appropriate standards for their particular disciplines and workflows. The latest addition to the SSK is a scenario for creating a born-digital dictionary in TEI.

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Not All Character N-grams Are Created Equal: A Study in Authorship Attribution – ACL Anthology

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.

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Transkribus & Magazines: Transkribus’ Transcription & Recognition Platform (TRP) as Social Machine…

Transkribus & Magazines: Transkribus’ Transcription & Recognition Platform (TRP) as Social Machine…

Introduction: This article proposes establishing a good collaboration between FactMiners and the Transkribus project that will help the Transkribus team to evolve the “sustainable virtuous” ecosystem they described as a Transcription & Recognition Platform — a Social Machine for Job Creation & Skill Development in the 21st Century!

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Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm | Digital Humanities

Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm | Digital Humanities

Introduction: Apart from its buoyant conclusion that authorship attribution methods are rather robust to noise (transcription errors) introduced by optical character recognition and handwritten text recognition, this article also offers a comprehensive read on the application of sophisticated computational techniques for testing and validation in a data curation process. 

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