Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution
10078Authors
nan
Abstract
AbstractTraditional Authorship Attribution models extract normalized counts of lexical elements such as nouns, common words and punctuation and use these normalized counts or ratios as features for author fingerprinting. The text is viewed as a bag-of-words and the order of words and their position relative to other words is largely ignored. We propose a new method of feature extraction which quantifies the distribution of lexical elements within the text using Kolmogorov complexity estimates. Testing carried out on blog corpora indicates that such measures outperform ratios when used as features in an SVM authorship attribution model. Moreover, by adding complexity estimates to a model using ratios, we were able to increase the F-measure by 5.2-11.8%
Keywords
Full Content
nan
Labeled Text
AbstractTraditional Authorship Attribution models extract normalized counts of lexical elements such as nouns, common words and punctuation and use these normalized counts or ratios as features for author fingerprinting. The text is viewed as a bag-of-words and the order of words and their position relative to other words is largely ignored. We propose a new method of feature extraction which quantifies the distribution of lexical elements within the text using Kolmogorov complexity estimates. Testing carried out on blog corpora indicates that such measures outperform ratios when used as features in an SVM authorship attribution model. Moreover, by adding complexity estimates to a model using ratios, we were able to increase the F-measure by 5.2-11.8%
Publication Details
- Paper ID:
- 10078
- Year:
- 2008
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan