GROBID: when data extraction becomes a suite

GROBID: when data extraction becomes a suite

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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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.]

Zur Epistemologie digitaler Methoden in den Geisteswissenschaften

Introduction: What is the precise impact of digital humanities on the humanities in general? That this influence exists seems a given, but how the digital humanities impact humanities methodology en epistemology is still an open question. This article delves deeper into this problem of epistemology and presents a model of five ‘polarities’ along which these influences can be positioned.

Teaching Quantitative Methods: What Makes It Hard (in Literary Studies)

Introduction: This article reflects on the lessons learnt by the author as he first taught a graduate course in digital analysis of literary texts. He stresses the importance of methodologies over technologies, the need for well-curated, community-created teaching datasets and the implications of the practical, discipline-based organisation of the curricula.