Determining if two documents are written by the same author

Koppel, Moshe; Winter, Yaron

doi:10.1002/asi.22954

Cited by 169 publications

(188 citation statements)

References 25 publications

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“…Similar to PAN-2013, the overall winner was a modification of the Impostors method [26]. The performance of this approach was notably stable on all six corpus subsets.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Compared to the closed-set attribution scenario, this setting is much more difficult, especially if the size of the candidate author set is small [24]. Finally, if the set of candidate authors is singleton, we get the author verification problem, which is a fundamental problem in authorship attribution since any problem setting can be decomposed into a series of verification problems [26].…”

Section: Author Identificationmentioning

confidence: 99%

“…Previous work on author verification has been evaluated using sample texts in one language only (Greek [60], Dutch [17,30], English [25,26]) and a specific genre (newspaper articles [60], student essays [30], fiction [25], newswire stories [19], poems [19], blogs [26]). Author verification was also included in previous editions of PAN: the author identification task at PAN-2011 included three author verification problems [1], PAN-2013 focused on author verification and provided corpora in English, Greek, and Spanish [22].…”

Section: Related Workmentioning

confidence: 99%

“…A variety of performance measures have been used in previous work on this task including false acceptance and false rejection rates [60,17], accuracy [25,26], recall, precision, F 1 [30], balanced error rate [19], recall-precision graphs [26] macro-average precision and recall [1], and ROC graphs [22]. Unfortunately, these measures are not able to explicitly estimate the ability of an approach to leave problems unanswered-a fact which is crucial in a cost-sensitive task like this.…”

Section: Related Workmentioning

confidence: 99%

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Improving the Reproducibility of PAN’s Shared Tasks:

Potthast

Gollub

Rangel

et al. 2014

Lecture Notes in Computer Science

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Potthast, M.; Gollub, T.; Rangel, F.; Rosso, P.; Stamatatos, E.; Abstract This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling.To improve the reproducibility of shared tasks in general, and PAN's tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.

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“…Similar to PAN-2013, the overall winner was a modification of the Impostors method [26]. The performance of this approach was notably stable on all six corpus subsets.…”

Section: Evaluation Resultsmentioning

confidence: 99%

Section: Author Identificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Reproducibility of PAN’s Shared Tasks:

Potthast

Gollub

Rangel

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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“…A number of diverse applications of text classification were reported in literature, ranging from subject categorization [3], analysis of sentiment of reviews or opinions, to authorship recognition of documents [4], [8], [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning methods for Subject Text Classification of Articles

Semberecki¹,

Maciejewski²

2017

Annals of Computer Science and Information Systems

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Abstract-This work presents a method of classification of text documents using deep neural network with LSTM (long shortterm memory) units. We have tested different approaches to build feature vectors, which represent documents to be classified: we used feature vectors constructed as sequences of words included in the documents, or, alternatively, we first converted words into vector representations using word2vec tool and used sequences of these vector representations as features of documents. We evaluated feasibility of this approach for the task of subject classification of documents using a collection of Wikipedia articles representing 7 subject categories. Our experiments show that the approach based on an LSTM network with documents represented as sequences of words coded into word2vec vectors outperformed a standard, bag-of-word approach with documents represented as frequency-of-words feature vectors.

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Identifying ISI‐indexed articles by their lexical usage: A text analysis approach

Moohebat

Raj

Kareem

et al. 2014

Asso for Info Science & Tech

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This research creates an architecture for investigating the existence of probable lexical divergences between articles, categorized as Institute for Scientific Information (ISI) and non-ISI, and consequently, if such a difference is discovered, to propose the best available classification method. Based on a collection of ISI- and non-ISI-indexed articles in the areas of business and computer science, three classification models are trained. A sensitivity analysis is applied to demonstrate the impact of words in different syntactical forms on the classification decision. The results demonstrate that the lexical domains of ISI and non-ISI articles are distinguishable by machine learning techniques. Our findings indicate that the support vector machine identifies ISI-indexed articles in both disciplines with higher precision than do the Naïve Bayesian and K-Nearest Neighbors techniques

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Determining if two documents are written by the same author

Cited by 169 publications

References 25 publications

Improving the Reproducibility of PAN’s Shared Tasks:

Improving the Reproducibility of PAN’s Shared Tasks:

Deep Learning methods for Subject Text Classification of Articles

Identifying ISI‐indexed articles by their lexical usage: A text analysis approach

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