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TRACER TUTORIAL: TEXT REUSE DETECTION SELECTION
Marco B¨ uchler, Emily Franzini and Greta Franzini
TABLE OF CONTENTS
- 1. What is Selection?
- 2. Selection techniques
- 3. Hacking
- 4. Conclusion and revision
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TRACER TUTORIAL: TEXT REUSE DETECTION SELECTION Mar co B uchler, - - PowerPoint PPT Presentation
TRACER TUTORIAL: TEXT REUSE DETECTION SELECTION Mar co B uchler, - - PowerPoint PPT Presentation
TRACER TUTORIAL: TEXT REUSE DETECTION SELECTION Mar co B uchler, Emily Franzini and Greta Franzini TABLE OF CONTENTS 1. Wha t is Selection? 2. Selection techniques 3. Hacking 4. Conclusion and revision 2/29 REMINDER: CURRENT APPROACH 3/29
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REMINDER: CURRENT APPROACH
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WHAT IS SELECTION?
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QUESTION
What do you associate with Selection?
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A VISUALISATION OF FEATURING
From biometry:
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SOME VOCABULARY
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SOME DEFINITIONS
- Global ”knowledge”: Information derived from the entire corpus;
- Local ”knowledge”: Information derived from the reuse unit (e.g. a
sentence);
- Global ”usage”: Selection is applied to e.g. the entire word list;
- Local ”usage”: Selection is applied to the reuse unit (e.g. a sentence).
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SELECTION TECHNIQUES
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SELECTION: GLOBAL ”KNOWLEDGE” & GLOBAL ”USAGE”
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SELECTION: GLOBAL ”KNOWLEDGE” & LOCAL ”USAGE”
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SELECTION: LOCAL ”KNOWLEDGE” & GLOBAL ”USAGE”
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SELECTION: LOCAL ”KNOWLEDGE” & LOCAL ”USAGE”
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SELECTION: MATRIX STYLE
A C D F G E B H I J K s1 1 1 1 1 1 s2 1 1 1 1 1 s3 1 1 1 1 1 s4 1 1 1 1 1 s5 1 1 1 1 1 = F A C D F G E s1 1 1 1 1 s2 1 1 1 1 1 s3 1 1 1 1 1 s4 1 1 1 1 1 s5 1 = S
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HOW TO MAKE THE DIFFERENT SELECTION STRATEGIES COMPARABLE?
- Different Selection strategies would require different parameters.
- This makes comparisons between Selection strategies difficult.
- For this reason, we introduce the Feature Density:
F = n
i=1
m′
j=1 sij
n
i=1
m
j=1 fij 15/29
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HACKING
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HACKING: CONFIGURATION
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HACKING
Tasks:
- Run on your own texts with different Preprocessing and Featuring
techniques ...
- eu.etrap.tracer.selection.localglobal.
LocalMaxFeatureFrequencySelectorImpl
- With different feature densities of 0.4, 0.6, 0.75
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HACKING
Questions:
- Run the aforementioned tasks. Compare the resulting ”tail
distributions” (you find all the information in the Selection folder in e.g. *.meta).
- Compare the tail distribution between Featuring and Selection.
Which influence does the Selection strategy have?
- Compare the .sel-files for the different Selection strategy (use
Microsoft Excel or OpenOffice to open the Selection file; sort by columns B and C).
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CONFIGURING THE SELECTION IMPL PARAMETER
Hint: The configuration file can be found in: $TRACER HOME/conf/tracer conf.xml
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FEATURE CORRELATION
Stimulus Response prob. Number of prob’s Co-occurrence Significance Butter Bread 60 Bread 51 Soft 40 Cheese 49 Milk 32 Sugar 29 Margarine 27 Milk 23 Cheese 20 Margarine 22 Fat 16 Farina 18 Yellow 14 Eggs 16 Bread and butter 8 Pound 14 Box/can 6 Meat 13 Eat 6
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CONTRASTIVE SEMANTICS
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GAP BETWEEN KNOWLEDGE AND EXPERIENCE
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CONCLUSION AND REVISION
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CHECK
How do Preprocessing and Featuring influence Selection?
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IMPORTANCE OF SELECTION
- Quality of the digital representation of a reuse unit;
- Speed for linking reuse units.
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FINITO!
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CONTACT
Team Marco B¨ uchler, Greta Franzini and Emily Franzini. Visit us http://www.etrap.eu contact@etrap.eu
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LICENCE
The theme this presentation is based on is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Changes to the theme are the work of eTRAP.
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