Approaches for Source Retrieval and Text Alignment of Plagiarism - - PowerPoint PPT Presentation

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Approaches for Source Retrieval and Text Alignment of Plagiarism - - PowerPoint PPT Presentation

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Approaches for Source Retrieval and Text Alignment of Plagiarism Detection Kong Leilei, Qi Haoliang, Du Cuixia, Wang Mingxing, Han Zhongyuan www.hljit.edu.cn PAN@CLEF2013 1 Who are we? 2 Who are we? 2 Who are we? 2 Who are we?

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Kong Leilei, Qi Haoliang, Du Cuixia, Wang Mingxing, Han Zhongyuan

www.hljit.edu.cn PAN@CLEF2013

Approaches for Source Retrieval and Text Alignment of Plagiarism Detection

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Who are we?

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Who are we?

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Who are we?

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Who are we?

Heilongjiang Institute of Technology Harbin, Heilongjiang Province, China

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Our University

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Index

 Approaches for Source Retrieval  Approaches for Text Alignment  Further works Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Source Retrieval

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Candidate Documents Suspicious plagiarism text Document Set Internet Resource Text Alignment Source Retrieval Suspicious document Query keywords

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Source Retrieval

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Candidate Documents Suspicious plagiarism text Document Set Internet Resource Text Alignment

Source Retrieval

Suspicious document Query keywords

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2 problmes of source retrieval

 Tow core problem of source retrieval

 Retrieval source is millions of documents

from the Internet

 This work was done by PAN

 The query keywords of suspicious document

which would be used for retrieval are not specified

 How to extract query keyword is one of important

issues of our work

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Query Keywords Extraction

 Query Keywords Extraction Based on TF-IDF  Query Keywords Extraction Based on Weighted

TF-IDF

 Adjacent Query Keywords Extraction by PatTree  Combination of Queries and Execution of Retrieval

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Keywords Based on TF-IDF

 TF - term frequency, denotes the frequency of term i in

document j

 IDF - inverse document frequency

IDF =log2 (N/ df j)

 TF-IDF of term i is:  Tips: we found that the top 10 terms with the highest TF-IDF

can obtain a good results

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Keywords Based on Weighted TF-IDF

 Weighted TF-IDF  Where weight is a weighted parameter, and we calculate

the weight of term i according to its location

 Tips: the keywords extraction based on the weighted TF-IDF

sometimes is useful, sometimes useless.

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Adjacent Query Keywords Extraction by PatTree

 The adjacent string with high frequency is more

important than a single word

 We use PatTree - an efficient data structure– to get the

adjacent strings and their frequency

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013 example

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Combination of Queries

Query Query Keywords 1 2 3 4 5 6 7 8 9 Top 1 to 5 query keywords based on TF-IDF Top 2 to 10 query keywords based on TF-IDF 2-Gram query keywords based on PatTree 3-Gram query keywords based on PatTree 4-Gram query keywords based on PatTree 4-Gram query keywords based on PatTree Top 1 to 5 query keywords based on weighted TF-IDF Top 6 to 10 query keywords based on weighted TF-IDF 5-Gram query keywords based on PatTree

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Table 1: Query Combination and Group

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Results Source Retrieval subtask

Workload Queries 48.50 Downloads 5691.47 Time to 1st Detection Queries 2.46 Downloads 285.66 Retrieved Performance Precision 0.01 Recall 0.65 No Detection 3

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Table2：Results Source of PAN@CLEF2013 Retrieval subtask

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Results Source Retrieval subtask

Workload Queries 48.50 Downloads 5691.47 Time to 1st Detection Queries 2.46 Downloads 285.66 Retrieved Performance Precision 0.01 Recall 0.65 No Detection 3

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Table2：Results Source of PAN@CLEF2013 Retrieval subtask

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Results Source Retrieval subtask

Workload Queries 48.50 Downloads 5691.47 Time to 1st Detection Queries 2.46 Downloads 285.66 Retrieved Performance Precision 0.01 Recall 0.65 No Detection 3

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Table2：Results Source of PAN@CLEF2013 Retrieval subtask

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24 Candidate Documents Suspicious plagiarism text Document Set Internet Resource

Text Alignment

Text Alignment Source Retrieval

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Suspicious document Query keywords

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25 Candidate Documents Suspicious plagiarism text Document Set Internet Resource

Text Alignment

Text Alignment

Source Retrieval

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

Suspicious document Query keywords

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Text Alignment

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Text Alignment

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Text Alignment

Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Text Alignment

Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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30 Match Merging

Text Alignment

Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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31 Match Merging

Text Alignment

Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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32 Match Merging

Text Alignment

Extraction Filtering Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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33 Match Merging

Text Alignment

Extraction Filtering Seeding

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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34 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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35 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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36 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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37 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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38 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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39 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

  • Bilateral Alternating

Merging Algorithm

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40 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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41 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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42 Seeding Match Merging

Text Alignment

Extraction Filtering

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Performance on the PAN2012 test corpus

Table 3: Overall evaluation results for the final test corpus

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Performance on the PAN2012 test corpus

Table 4: Results for the 02-no-obfuscation sub-corpus

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Performance on the PAN2012 test corpus

Table 5: Results for the 03-random-obfuscation

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Performance on the PAN2012 test corpus

Table 6: Results for the 04-translation-obfuscation

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Performance on the PAN2012 test corpus

Table 7: Evaluation results for the 05-summary-obfuscation

Heilongjiang Institute of Technology, Kong Leilei PAN@CLEF2013

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Further work

 Use different methods to deal with different plagiarism

problems to obtain a better performance

 Query keywords extraction and ranking

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Thank you for your attention!