Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Linguistic profiling for author recognition and verification
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Linguistic profiling of texts for the purpose of language verification
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Foundations and Trends in Information Retrieval
Speaker Classification by Means of Orthographic and Broad Phonetic Transcriptions of Speech
Speaker Classification II
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
e-mail authorship verification for forensic investigation
Proceedings of the 2010 ACM Symposium on Applied Computing
Language Resources and Evaluation
Investigative behavior profiling with one class SVM for computer forensics
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
A novel approach of mining write-prints for authorship attribution in e-mail forensics
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Syntactic dependency-based n-grams as classification features
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Syntactic N-grams as machine learning features for natural language processing
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
This article explores the effects of parameter settings in linguistic profiling, a technique in which large numbers of counts of linguistic features are used as a text profile which can then be compared to average profiles for groups of texts. Although the technique proves to be quite effective for authorship verification, with the best overall parameter settings yielding an equal error rate of 3% on a test corpus of student essays, the optimal parameters vary greatly depending on author and evaluation criterion.