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1 Canonical Text Service Jochen Tiepmar BigData Competence Center ScaDS Naural Language Processing Leipzig University
2 Survey From to Anonym, no tracking, skipping allowed Recall : 9 100% know the term terabyte and 71,43% know the term petabyte.
3 Overview Canonical Text Services (CTS) protocol for a webbased citable text service Unique Identifiers(Unique Resource Name, URN) refer to text passages Developed in Homer Multitext Project( Smith et.al This implementation was done in Billion Words Project Implementation for Tripelstore and XML-DB not suitable for BW-Project Demo webpage:
4 Documents Hierarchy Shakespeare, Sonnet 1, Vers 1 Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
5 Citation Document outer hierarchy Shakespeare Sonnets english 1st edition Text passage inner hierarchy Sonnet 1 Vers 1 Combined Shakespeare Sonnets english 1st edition Sonnet 1 Vers 1 CTS-URN urn:cts:demo:shakespeare.sonnets.en.1:1.1
6 Canonical Citation urn:cts:demo:shakespeare.sonnets: urn:cts:demo:shakespeare.sonnets.de: Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
7 Canonical Citation urn:cts:demo:shakespeare.sonnets:35.4 Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
8 Canonical Citation urn:cts:demo:shakespeare.sonnets:35 Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
9 Canonical Citation urn:cts:demo:shakespeare.sonnets: urn:cts:demo:shakespeare.sonnets: Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
10 Canonical Citation Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
11 Canonical Citation Shakespeare Sonnets Sonnet 1 Sonnet 35 Sonnet 154 Vers 1 Vers 5 Word 1 Word 10
12 Mapping URNs -> Text :1 :1.1 :1.1.1 O Tannenbaum, O Tannenbaum, :1.1.2 Wie treu sind deine Blätter. :1.1.3 Du grünst nicht nur zur Sommerzeit, :1.1.4 Nein auch im Winter wenn es schneit. :1.1.5 O Tannenbaum, O Tannenbaum, :1.1.6 Wie grün sind deine Blätter! :1.2 :1.2.1 O Tannenbaum, O Tannenbaum, :1.2.2 Du kannst mir sehr gefallen! :1.2.3 Wie oft hat schon zur Winterszeit :1.2.4 Ein Baum von dir mich hoch erfreut! :1.2.5 O Tannenbaum, O Tannenbaum, :1.2.6 Du kannst mir sehr gefallen! :1.3 :1.3.1 O Tannenbaum, O Tannenbaum, :1.3.2 Dein Kleid will mich was lehren: :1.3.3 Die Hoffnung und Beständigkeit :1.3.4 Gibt Mut und Kraft zu jeder Zeit! :1.3.5 O Tannenbaum, O Tannenbaum, :1.3.6 Dein Kleid will mich was lehren.
13 Using CTS to standardize texts Differentiate text structure from text content and meta information Refer to generic text parts Reduce type of text part to label 8/9 think that standardizing documents and access to documents will be (very) important in the next 10 years 8/9 think that referencing documents based on structural text parts (like chapter or sentence) is reasonable. 1 suggests named entities, 1 adds that further standardization and more flexibility is needed
14 Div-View urn:cts:songs:christmas.ohtennenbaum.de.1: <passage> </passage> <passage> </div1> </passage> O Tannenbaum, O Tannenbaum, ( ) Wie grün sind deine Blätter! O Tannenbaum, O Tannenbaum, ( ) Ein Baum von dir mich hoch erfreut! <div1 n="1" type="song"> <div2 n="1" type="strophe"> <div3 n="1" type="line">o Tannenbaum, O Tannenbaum, </div3> ( ) <div3 n="6" type="line">wie grün sind deine Blätter! </div3> </div2> <div2 n="2" type="strophe"> <div3 n="1" type="line">o Tannenbaum, O Tannenbaum, </div3> ( ) <div3 n= 6" type="line">ein Baum von dir mich hoch erfreut!</div3> </div2>
15 Generic Reader 2014 Leipzig University // Martin Reckziegel
16 CTS Cloning URNs of <div>s can be used to reconstruct URNs -> Content of one CTS can be cloned Data can be narrowed down from left to right by URNs Clone everything from Shakespeare: urn:cts:demo:shakespeare.sonnets.en.1:1.1 <passage> <div1 n="1" type="song"> <div2 n="1" type="strophe"> <div3 n="1" type="line"> </div3> </div2> <div2 n="2" type="strophe"> <div3 n="1" type="line"> </div3> </div2> </div1> </passage>
17 CTS Cloning Backup Data 7/9 think that a decentralized web of smaller text repositories based on individual researchers or projects is a more realistic scenario than a few central big text repositories containing the digitized documents of multiple researchers or projects
18 Data Text Collection Languages Documents File size A german daily newspaper German ,2 gb Deutsches Textarchiv German gb PBC 831 Translations 831 1,9 gb Perseus Greek, Latin mb Law German mb German Shakespeare works German mb
19 Alignment ( )
20 Text Reuse Analysis Which text part is a citation of what text part? Pre calculation necessary -> calculate similiarity between sentence and all other sentences -> high similiarity = citation candidate -> cross comparison, misses need to be calculated Result: text reuse graph
21 Text Reuse Analysis per CTS URNs as IDs for text parts Fulltext search (WIP) as similiarity search Unique IDs + fulltext search => Text Reuse Analysis? To be continued( )
22 CTS Text Miner (CTSTM) CTS Text Mining Framework Broad and comprehensive framework for text analysis Done: Term-Document Matrix Token/Types per Document/Corpus Document- and Termbased Pruning + lists of Stopwords Tokensequence /(Kookurenz)
23 CTS Admin Tool Implemented by Sascha Ludwig
24 Big Picture Backup Data global decentralised community organised community backup ed open access standardized persistent citable easy to install text repository for browsing, searching and analysis of text resources.
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