Computational methods for the history of philosophy: Interpretative models and corpus-building. Ginammi, A. & Sommerauer, P. November, 2019. ![Computational methods for the history of philosophy: Interpretative models and corpus-building [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 1 download In what way can computational and quantitative methods supplement the largely qualitative methods of traditional history of philosophy? In this talk, we will give some answers to that question, by means of presenting several projects in the history of philosophy using computational methods carried out within our team. The methods applied in these projects are rather different and range from count-based, highly basic computational techniques, to rather sophisticated information retrieval tools based on distributional semantics, to the use of formal ontologies. What these projects have in common is that they use novel methods and thereby urge us to reflect on the methodology: how can these methods be used in a transparent and reliable manner? The majority of our projects employs the ``model-approach” which we introduced in (removed for blind review), i.e. they use explicit conceptual frameworks which facilitate the interpretation of texts. We argue that the model approach is a useful method to obtain philosophically relevant information from the data delivered by quantitative computational methods, and at the same time makes the interpretation of this data by researchers less arbitrary and biased. One of our projects supplements this model approach by defined annotation schemes to capture differences and similarities between various conceptions in a way which is as accountable as possible. An undeniable benefit of computational methods is that research can be done on a scale which is unthinkable for traditional methods. However, this raises a new issue which, in our view, hitherto received insufficient attention: how to build a representative corpus relative to a given historical research question? We discuss our efforts to build our datasets as systematically as possible, and propose that corpus-building for historical-interpretive research should rely on a method taking inspiration from the so-called “systematic literature review” common in the sciences. Additionally, we will discuss the issues we encountered by building multilingual, multi-script corpora for our computational research.
@misc{ginammi_computational_2019,
title = {Computational methods for the history of philosophy: {Interpretative} models and corpus-building},
url = {https://docs.google.com/presentation/d/1sWTwE0xoF5jOdkS7uG5EuaWxD2xKyXfWd7mYJ2I9-No/edit#slide=id.g6b2111c0b8_0_0},
abstract = {In what way can computational and quantitative methods supplement the largely qualitative methods of
traditional history of philosophy? In this talk, we will give some answers to that question, by means of
presenting several projects in the history of philosophy using computational methods carried out within
our team. The methods applied in these projects are rather different and range from count-based, highly
basic computational techniques, to rather sophisticated information retrieval tools based on
distributional semantics, to the use of formal ontologies. What these projects have in common is that
they use novel methods and thereby urge us to reflect on the methodology: how can these methods be
used in a transparent and reliable manner?
The majority of our projects employs the ``model-approach” which we introduced in (removed for blind
review), i.e. they use explicit conceptual frameworks which facilitate the interpretation of texts. We
argue that the model approach is a useful method to obtain philosophically relevant information from
the data delivered by quantitative computational methods, and at the same time makes the
interpretation of this data by researchers less arbitrary and biased. One of our projects supplements this
model approach by defined annotation schemes to capture differences and similarities between various
conceptions in a way which is as accountable as possible.
An undeniable benefit of computational methods is that research can be done on a scale which is
unthinkable for traditional methods. However, this raises a new issue which, in our view, hitherto
received insufficient attention: how to build a representative corpus relative to a given historical
research question? We discuss our efforts to build our datasets as systematically as possible, and
propose that corpus-building for historical-interpretive research should rely on a method taking
inspiration from the so-called “systematic literature review” common in the sciences. Additionally, we
will discuss the issues we encountered by building multilingual, multi-script corpora for our
computational research.},
author = {Ginammi, Annapaola and Sommerauer, Pia},
collaborator = {Oortwijn, Yvette and Meyer, François and Betti, Arianna and Fokkens, Antske and Bloem, Jelke},
month = nov,
year = {2019},
}
Downloads: 1
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One of our projects supplements this model approach by defined annotation schemes to capture differences and similarities between various conceptions in a way which is as accountable as possible. An undeniable benefit of computational methods is that research can be done on a scale which is unthinkable for traditional methods. However, this raises a new issue which, in our view, hitherto received insufficient attention: how to build a representative corpus relative to a given historical research question? We discuss our efforts to build our datasets as systematically as possible, and propose that corpus-building for historical-interpretive research should rely on a method taking inspiration from the so-called “systematic literature review” common in the sciences. 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