A data structure for representing multi-version texts online

Schmidt, Desmond; Colomb, Robert M.

doi:10.1016/j.ijhcs.2009.02.001

Cited by 49 publications

(22 citation statements)

References 40 publications

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“…It has been used extensively in a number of different Natural Language Processing projects [27] and as a result it has had its effectiveness in the field proven. The multitude of texts that are found within it allow for sentences from it to be a fairer representation of the English language than using a corpus that is more focused on a single source would be.…”

Section: Selecting a Corpusmentioning

confidence: 99%

An automatic corpus based method for a building Multiple Fuzzy Word Dataset

Chandran

Crockett

McLean

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Fuzzy sentence semantic similarity measures are designed to be applied to real world problems where a computer system is required to assess the similarity between human natural language and words or prototype sentences stored within a knowledge base. Such measures are often developed for a specific corpus/domain where a limited set of words and sentences are evaluated. As new "fuzzy" measures are developed the research challenge is on how to evaluate them. Traditional approaches have involved rigorous and complex human involvement in compiling benchmark datasets and obtaining human similarity measures. Existing datasets often contain limited fuzzy words and do allow the fuzzy measures to be exhaustively tested. This paper presents an automatic method for the generation of a Multiple Fuzzy Word Dataset (MFWD) from a corpus. A Fuzzy Sentence PairingAlgorithm is used to extract and augment high, medium and low similarity sentence pairs with multiple fuzzy words. Human ratings are collected through crowdsourcing and the MFWD is evaluated using both fuzzy and traditional sentence similarity measures. The results indicated that fuzzy measures returned a higher correlation with human ratings compared with traditional measures.

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Section: Selecting a Corpusmentioning

confidence: 99%

An automatic corpus based method for a building Multiple Fuzzy Word Dataset

Chandran

Crockett

McLean

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…To this end CollateX adopts Schmidt and Colomb's concept of a variant graph (Schmidt and Colomb, 2009). We will demonstrate this process using the case of a variant token.…”

Section: Comparing Texts With Collatexmentioning

confidence: 99%

Computer-supported collation of modern manuscripts: CollateX and the Beckett Digital Manuscript Project

Dekker¹,

Hülle²,

Middell³

et al. 2014

Digital Scholarship Humanities

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Interoperability is the key term within the framework of the European-funded research project Interedition, 1 whose aim is 'to encourage the creators of tools for textual scholarship to make their functionality available to others, and to promote communication between scholars so that we can raise awareness of innovative working methods.' The tools developed by Interedition's 'Prototyping' working group needed to be tested by other research teams, which formulate strategic recommendations. To this purpose, the Centre for Manuscript Genetics (University of Antwerp), the Huygens Institute for the History of the Netherlands (The Hague) and the University of Würzburg have been working together within the framework of Interedition.One of the concrete results of collaboration is the development and fine-tuning of the text collation tool CollateX. 2 In this paper we would like to investigate how the architecture of a digital archive containing modern manuscripts can be designed in such a way that users can autonomously collate textual units of their choice with the help of the collation tool CollateX and thus decide for themselves how this digital architecture functions -as an archive, as a genetic dossier, or as an edition. The first part introduces CollateX and its internal concepts and heuristics as a tool for digitally supported collation. How this tool can be integrated in the infrastructure of an electronic edition is discussed in part two. The third and final part examines the possibility of deploying CollateX for the collation of modern manuscripts by means of a test CollateX is an open source Java collation engine, developed, maintained, and supported by an international open source development community (OSDC) of developers active in the digital humanities community. Having originated and finding a number of applications in several projects of the Huygens Institute for the History of the Netherlands, the final coordination of development currently rests with this institute. However the code is in the public domain: https://github.com/interedition/collatex case: the Beckett Digital Manuscript Project (www.beckettarchive.org). Computer supported collation with CollateXFollowing John Unsworth's textual scholarship workflow typology of scholarly primitives (Unsworth, 2000), it is clear that text comparison is pivotal to any kind of textual scholarship. The role of text comparison becomes paramount in any scholarly editing project that involves critical enquiries about the edited text witnessed in multiple versions. Conducting such a comparison of a text's versions manually or -to use the proper terminology of the field -'collating them' classifies as tedious and error-prone work, 3 especially as the required attention to detail is highly demanding when measured against the repetitive and (sometimes) mechanical nature of the task. From that perspective, this type of work seems an ideal candidate for automation, not only because computers can support users in tedious, error-prone duties rather efficiently, but sp...

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“…The idea of the variant graph was first introduced by Schmidt and Colomb (2009) and is used in the 'nmerge' tool of Schmidt; an alternative form of variant graph was developed for the CollateX tool (Dekker and Middell, 2011). The variant graph in either form is an elegant way to represent concurrence as well as variation within a text tradition; one may imagine a text running from beginning to end with a number of points of divergence, where each witness takes a single path through these divergences.…”

Section: An Empirical Model For Text Variationmentioning

confidence: 99%

Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata

Andrews

Macé

2013

Literary and Linguistic Computing

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Stemmatology, or the reconstruction of the transmission history of texts, is a field that stands particularly to gain from digital methods. Many scholars already take stemmatic approaches that rely heavily on computational analysis of the collated text (e.g. Robinson and O'Hara 1996;Salemans 2000;Heikkilä 2005;Windram et al. 2008 among many others). Although there is great value in computationally assisted stemmatology, providing as it does a reproducible result and allowing access to the relevant methodological process in related fields such as evolutionary biology, computational stemmatics is not without its critics. The current state-ofthe-art effectively forces scholars to choose between a preconceived judgment of the significance of textual differences (the Lachmannian or neo-Lachmannian approach, and the weighted phylogenetic approach) or to make no judgment at all (the unweighted phylogenetic approach). Some basis for judgment of the significance of variation is sorely needed for medieval text criticism in particular. By this, we mean that there is a need for a statistical empirical profile of the textgenealogical significance of the different sorts of variation in different sorts of medieval texts. The rules that apply to copies of Greek and Latin classics may not apply to copies of medieval Dutch story collections; the practices of copying authoritative texts such as the Bible will most likely have been different from the practices of copying the Lives of local saints and other commonly adapted texts. It is nevertheless imperative that we have a consistent, flexible, and analytically tractable model for capturing these phenomena of transmission. In this article, we present a computational model that captures most of the phenomena of text variation, and a method for analysis of one or more stemma hypotheses against the variation model. We apply this method to three 'artificial traditions' (i.e. texts copied under laboratory conditions by scholars to study the properties of text variation) and four genuine medieval traditions whose transmission history is known or deduced in varying degrees. Although our findings are necessarily limited by the small number of texts at our disposal, we demonstrate here some of the wide variety of calculations that can be made using our model. Certain of our results call sharply into question the utility of excluding 'trivial' variation such as orthographic and spelling changes from stemmatic analysis.

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A data structure for representing multi-version texts online

Cited by 49 publications

References 40 publications

An automatic corpus based method for a building Multiple Fuzzy Word Dataset

An automatic corpus based method for a building Multiple Fuzzy Word Dataset

Computer-supported collation of modern manuscripts: CollateX and the Beckett Digital Manuscript Project

Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata

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