Система визначення авторства тексту

Було розроблено систему ідентифікації та перевірки авторства документа, побудовану на основі машинного навчання. Оригінальність моделі обумовлена запропонованим унікальним профілем ознак автора, що дозволив, із застосуванням методу опорних векторів (SVM), отримати високі показники точності.A new effective system for identification and verification of text authorship has been developed. The system is created on the base of machine learning. The originality of the proposed model is caused by the unique profile of the author attributes that allows getting extra-high performance accuracy using the method of the Support Vector Machine (SVM)

A Deep Context Grammatical Model For Authorship Attribution

We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training cases. We then outline the attribution process – assigning authorship to the model which yields the highest probability for the given test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts

The use of orthogonal similarity relations in the prediction of authorship

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37256-8_38Recent work on Authorship Attribution (AA) proposes the use of meta characteristics to train author models. The meta characteristics are orthogonal sets of similarity relations between the features from the different candidate authors. In that approach, the features are grouped and processed separately according to the type of information they encode, the so called linguistic modalities. For instance, the syntactic, stylistic and semantic features are each considered different modalities as they represent different aspects of the texts. The assumption is that the independent extraction of meta characteristics results in more informative feature vectors, that in turn result in higher accuracies. In this paper we set out to the task of studying the empirical value of this modality specific process. We experimented with different ways of generating the meta characteristics on different data sets with different numbers of authors and genres. Our results show that by extracting the meta characteristics from splitting features by their linguistic dimension we achieve consistent improvement of prediction accuracy.This research was partially supported by ONR grant N00014-12-1-0217 and by NSF award 1254108. It was also supported in part by the CONACYT grant 134186 and by the European Commission as part of the WIQ-EI project (project no. 269180) within the FP7 People Programme.Sapkota, U.; Solorio, T.; Montes Gómez, M.; Rosso, P. (2013). The use of orthogonal similarity relations in the prediction of authorship. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 463-475. https://doi.org/10.1007/978-3-642-37256-8_38S463475Baker, L.D., McCallum, A.: Distributional clustering of words for text classification. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR, pp. 96–103. ACM, Melbourne (1998)Biber, D.: The multi-dimensional approach to linguistic analyses of genre variation: An overview of methodology and findings. Computers and the Humanities 26, 331–345 (1993)Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 1998 Conference on Computational Learning Theory (1998)Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information-theoretic feature clsutering algorithm for text classification. Journal of Machine Learning Research 3, 1265–1287 (2003)Escalante, H.J., Montes-y-Gómez, M., Solorio, T.: A weighted profile intersection measure for profile-based authorship attribution. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 232–243. Springer, Heidelberg (2011)Escalante, H.J., Solorio, T., Montes-y-Gomez, M.: Local histograms of character n-grams for authorship attribution. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 288–298. Association for Computational Linguistics, Portland (2011)Hayes, J.H.: Authorship attribution: A principal component and linear discriminant analysis of the consistent programmer hypothesis. I. J. Comput. Appl., 79–99 (2008)Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 77–86. Springer, Heidelberg (2006)Karypis, G.: CLUTO - a clustering toolkit. Tech. Rep. #02-017 (November 2003)Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram based author profiles for authorship attribution. In: Proceedings of the Pacific Association for Computational Linguistics, pp. 255–264 (2003)Koppel, M., Schler, J., Argamon, S.: Authorship attribution in the wild. Language Resources and Evaluation 45, 83–94 (2011)Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, pp. 513–520 (August 2008)Luyckx, K., Daelemans, W.: The effect of author set size and data size in authorship attribution. In: Literary and Linguistic Computing, pp. 1–21 (August 2010)Marneffe, M.D., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: LREC 2006 (2006)Plakias, S., Stamatatos, E.: Tensor space models for authorship identification. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 239–249. Springer, Heidelberg (2008)Raghavan, S., Kovashka, A., Mooney, R.: Authorship attribution using probabilistic context-free grammars. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 38–42. Association for Computational Linguistics, Uppsala (2010)Slonim, N., Tishby, N.: The power of word clusters for text classification. In: 23rd European Colloquium on Information Retrieval Research, ECIR (2001)Solorio, T., Pillay, S., Raghavan, S., Montes-y-Gómez: Generating metafeatures for authorship attribution on web forum posts. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 156–164. AFNLP, Chiang Mai (2011)Stamatatos, E.: Author identification using imbalanced and limited training texts. In: 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, pp. 237–241 (September 2007)Stamatatos, E.: Author identification: Using text sampling to handle the class imbalance problem. Information Processing and Managemement 44, 790–799 (2008)Stamatatos, E.: Plagiarism detection using stopword n-grams. Journal of the American Society for Information Science and Technology 62(12), 2512–2527 (2011)Stamatatos, E.: A survey on modern authorship attribution methods. Journal of the American Society for Information Science and Technology 60(3), 538–556 (2009)Stolcke, A.: SRILM - an extensible language modeling toolkit, pp. 901–904 (2002)Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, vol. 1, pp. 173–180 (2003)de Vel, O., Anderson, A., Corney, M., Mohay, G.: Multi-topic e-mail authorship attribution forensics. In: Proceedings of the Workshop on Data Mining for Security Applications, 8th ACM Conference on Computer Security (2001)Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005

Automated Detection of Sockpuppet Accounts in Wikipedia

Wikipedia is a free Internet-based encyclopedia that is built and maintained via the open-source collaboration of a community of volunteers. Wikipedia’s purpose is to benefit readers by acting as a widely accessible and free encyclopedia, a comprehensive written synopsis that contains information on all discovered branches of knowledge. The website has millions of pages that are maintained by thousands of volunteer editors. Unfortunately, given its open-editing format, Wikipedia is highly vulnerable to malicious activity, including vandalism, spam, undisclosed paid editing, etc. Malicious users often use sockpuppet accounts to circumvent a block or a ban imposed by Wikipedia administrators on the person’s original account. A sockpuppet is an “online identity used for the purpose of deception.” Usually, several sockpuppet accounts are controlled by a unique individual (or entity) called a puppetmaster. Currently, suspected sockpuppet accounts are manually verified by Wikipedia administrators, which makes the process slow and inefficient. The primary objective of this research is to develop an automated ML and neural-network-based system to recognize the patterns of sockpuppet accounts as early as possible and recommend suspension. We address the problem as a binary classification task and propose a set of new features to capture suspicious behavior that considers user activity and analyzes the contributed content. To comply with this work, we have focused on account-based and content-based features. Our solution was bifurcated into developing a strategy to automatically detect and categorize suspicious edits made by the same author from multiple accounts. We hypothesize that “you can hide behind the screen, but your personality can’t hide.” In addition to the above-mentioned method, we have also encountered the sequential nature of the work. Therefore, we have extended our analysis with a Long Short Term Memory (LSTM) model to track down the sequential pattern of users’ writing styles. Throughout the research, we strive to automate the sockpuppet account detection system and develop tools to help the Wikipedia administration maintain the quality of articles. We tested our system on a dataset we built containing 17K accounts validated as sockpuppets. Experimental results show that our approach achieves an F1 score of 0.82 and outperforms other systems proposed in the literature. We plan to deliver our research to the Wikipedia authorities to integrate it into their existing system

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Authorship attribution using probabilistic context-free grammars

In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach involves building a probabilistic context-free grammar for each author and using this grammar as a language model for classification. We evaluate the performance of our method on a wide range of datasets to demonstrate its efficacy.

In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), pp. 38--42 2010. Authorship Attribution Using Probabilistic Context-Free Grammars

In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach involves building a probabilistic context-free grammar for each author and using this grammar as a language model for classification. We evaluate the performance of our method on a wide range of datasets to demonstrate its efficacy.