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# Determining Common Authorship Among Documents - PowerPoint PPT Presentation

Determining Common Authorship Among Documents. Paul Bonamy Mentor: Dr. Paul Kantor. Author Identification & Common Authorship. Author Identification: “Who wrote this?” Mosteller/Wallace, 1964 – The Federalist 12 disputed papers attributed to Madison Generally utilizes statistical analysis

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### Determining Common Authorship Among Documents

Paul Bonamy

Mentor: Dr. Paul Kantor

• Author Identification: “Who wrote this?”

• Mosteller/Wallace, 1964 – The Federalist

• 12 disputed papers attributed to Madison

• Generally utilizes statistical analysis

• Common Authorship: “Do these share an author?”

• Does not (necessarily) require statistics/training

• Useful for detecting forgeries, etc

• Implements Bayesian Multinomial Regression

• Used to perform 1-of-k classification

• BMRtrain accepts feature vectors, outputs assignment model

• BMRclassify accepts model & vectors, outputs assignments

• Can output author probability vectors

• Consider two match boxes

• Probability of Box 1, given black marble?

• H0= We have Box 1, E = We see a black marble

• Bayes’ Theorem Extendable to P(C|F1…FN)

• C is a class

• F1…FNare features

• Effectively applies Bayes’ Theorem to itself

Data

( Doc Corpus)

Test/Train

Splitter

Training Set

Testing Set

Feature Extractor

Feature Vectors

Feature Vectors

BMRtrain

Model

BMRclassify

Author

Identification

Author

Probabilities

Author

Probabilities

• Articles from 2006-07 issues of The Compass Newspaper

• 16 Authors

• 130 Documents

• 300 - 500 Words: 69

• 500+ Words: 61

• Varied Topics

• On Friday, November 3, LSSU experienced its first closing of the semester due to inclement weather. The Soo Evening News reported a “number of minor mishaps,” and “slippery-road induced mishaps,” including two crashes near the campus of LSSU. All classes before 10 AM were canceled because of the snow and ice that had accumulated overnight, but many students arrived for classes as usual, unaware of the cancellation. …

• Perl script using Lingua::EN::Tagger

• Selects words, part-of-speech (POS), or both (wordPOS)

• Used wordPOS in common authorship study

• Returns vector of feature frequencies

• 4:9.0 16:5.0 22:4.0 23:2.0 28:5.0 29:1.0 33:4.0 36:9.0 38:1.0 41:3.0 46:13.0 56:2.0 …

• Produced by BMR/BXR upon request

• Probability doc belongs to each author in the training set

• Not normalized (sum not necessarily 1)

• 0.17% 0.68% 9.13% 8.90% 2.42% 0.94% 10.55% 0.32% 0.72% 36.95% 0.31% 0.50% 0.48% 22.08% 1.34% 4.52%

• Select all distinct pairs of vectors

• Compute dot product and Euclidean distance

• Sort data

• Descending by dot product

• Ascending by Euclidean distance

• Select all distinct pairs of vectors

• Compute dot product and Euclidean distance

• Sort data

• Descending by dot product

• Ascending by Euclidean distance

• DP and Euclidean distance measure distance

• Computed distances between vectors

• Sorted from closest to furthest

• Docs by same author are close together

• Docs by different authors far apart

• Shows fractions of not-pairs versus fraction of pairs

• Area under curve indicates model accuracy

• Higher is better

• Euclidean distance of feature vector

• This curve: 64.7% of area under curve

• Obtained second corpus

• 9377 Documents

• 24 Authors

• Results similar to those on Compass dataset

• Are Area Under Curve variations significant?

• How does Author ID model accuracy affect same-author accuracy?

• A low Author-ID accuracy model did very well

• Can we reduce memory/processing requirements?