Peter Broadwell

Computational folkloristics

A searchable meta-graph can connect even troublesome house elves and other supernatural beings to scholarly folk categories.

Comparing published scientific journal articles to their pre-print versions

Academic publishers claim that they add value to scholarly communications by coordinating reviews and contributing and enhancing text during publication. These contributions come at a considerable cost: US academic libraries paid

Semi-Supervised Morphosyntactic Classification of Old Icelandic

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GhostScope: Conceptual Mapping of Supernatural Phenomena in a Large Folklore Corpus

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WitchHunter: Tools for the Geo-Semantic Exploration of a Danish Folklore Corpus

1.7 billion for serial subscriptions in 2008 alone. Library budgets, in contrast, are flat and not able to keep pace with serial price inflation. We have investigated the publishers’ value proposition by conducting a comparative study of pre-print papers from two distinct science, technology, and medicine corpora and their final published counterparts. This comparison had two working assumptions: (1) If the publishers’ argument is valid, the text of a pre-print paper should vary measurably from its corresponding final published version, and (2) by applying standard similarity measures, we should be able to detect and quantify such differences. Our analysis revealed that the text contents of the scientific papers generally changed very little from their pre-print to final published versions. These findings contribute empirical indicators to discussions of the added value of commercial publishers and therefore should influence libraries’ economic decisions regarding access to scholarly publications.

Digital Research Start-Up Partnerships @ UCLA Library

Classification is a vexing problem in folkloristics. Although broad genre classifications such as “ballad”, “folktale”, “legend”, “proverb”, and “riddle” are well established and widely accepted, these formal classifications are coarse and dolittle more than provide a first level sort on materials for collections that can easily include tens, if not hundreds, of thousands of records.

Intro to R / Network analysis

We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.