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by: Maryse Herman


Maryse Herman
GPA 3.68

Patricia Galloway

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About this Document

Patricia Galloway
Class Notes
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This 16 page Class Notes was uploaded by Maryse Herman on Monday September 7, 2015. The Class Notes belongs to INF 389J at University of Texas at Austin taught by Patricia Galloway in Fall. Since its upload, it has received 47 views. For similar materials see /class/181907/inf-389j-university-of-texas-at-austin in Information Studies at University of Texas at Austin.

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Date Created: 09/07/15
Testing appraisal models with i digital corpora Patricia Galloway School of Information University of Texas at Austin Q Why a ppraise Not enough room Moore s Law Not enough time for description Google Nobody will care about most of the material anyway the long tail sow appraise Reject outright Accept everything Accept partially reductive appraisal Accept by initial agreement only part of materials offered front end Perform granular processing appraisal back end What are the effects of reductive appraisal The ideal appraisal accurately chooses the best selection of materials for informational and evidentiary uses according to best knowledge of the time and without going broke How short of living forever to test how closely this ideal is reached Reductive appraisal as preemptive IR Is there a t between appraisal and conventional IR methods Appraisal as preemptive information retrieval IR selects desirable records but can always come back to original corpus Appraisal selects desirable records discards the rest original corpus is gone Can evaluation methods and measures borrowed from IR be used Testing appraisal effectiveness against digital corpora Digital corpora permit digital tools so digital corpora permit complete testing Sources of digital tools Corpus linguistics Literary analysis tools eg style authorship Text miningclustering Information retrieval evaluation tools 4 Proposed experiment Begin with corpus Use simple appraisal model to reduce corpus Measure information loss Example PKG s MDAH email PKG S MDAH email 1997 Sent only Attachments removed Consistent class of records No privacy concerns Potential for classification Topics Correspondents Appraisal modelling Can appraisal methods be modelled formally Maybe not simply cf Gilliland39s results Selection constraints Selection as decision tree Appraisal as data reduction process Appraisal data reduction Implementing data reduction procedure Reduction of corpus FBI appraisal decision tree based on results of the FBI appraisal Selection pro les applied to Sent97 systematic sample fat les Corpus preprocessing Isolating one message per file for entire corpus Tokenization of all messages including Removal of headers Stopword removal Stemming Derivation of reduced versions of corpus Preparing termdocument incidence and termdocument frequency matrices Calculating distance and similarity measures Analysis of reduced versions against original corpus Internal semantic structure clustering of tokens Network structu re correspondents dates Similarity in vector space Information gain or loss Larger project Creating multiple formal appraisal models from case studies in archival literature Testing appraisal models against appropriate digital corpora and each other Characterizing appraisal with more elaborate formal models Operationalizing implied model of record production provenance Appraisal modelled explicitly as data reduction process Discovering and specifying formal effects on content through automated content analysis Characterizing specific appraisal contexts Actual digital record corpus Formal methods for characterizing corpus Stakeholdercorpus actor network as provenance specification Correspondence analysis Effects of assumptions on selection procedures Evaluating appropriate 4 appraisal procedures Choosing a digital corpus As generic model for similar digital collections For analogous behavior to some paper collection of interest Testing against formal appraisal models Comparing results to appraisal goals Formally de ning appraisal method choice as a function of acceptable loss


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