[PDF] Top 20 Authorship Attribution and Verification with Many Authors and Limited Data

... author verification on eight authors, the linguistic profiling system (Van Hal- teren, 2005) has a False Reject Rate (FRR) of 0% and a False Accept Rate (FAR) of ...did authorship verification ... See full document

... the authorship research –which for a long time in the past was mainly focused on literary ques- tions of unknown or disputed authorship– drifts to- wards more practical applications in such domains as ... See full document

... 145 authors and limited training data (student essays on Artificial ...that Authorship Verification is much harder than Authorship Attribution, and that more ... See full document

... closed-set attribution where it is assumed that the author of the text under investigation is necessarily a member of a given well-defined set of candidate authors (Stamatatos et ...fits many ... See full document

... of authors. We report experi- mental results in AA data sets that confirm that LHs over character n-grams are more help- ful for AA than the usual global histograms, yielding results far superior to state ... See full document

... traditional authorship attribution, in our ...traditional authorship attribution ...have limited ourselves to these simple features as they can be calculated very rapidly and without ... See full document

... of authorship attribution is one of the major areas of text ...and limited user verification possibilities, im- portance of verifying the authorship of tweets and other short texts ... See full document

... For all datasets, early stopping was used on the development sets and models trained with the Adam update rule (Kingma and Ba, 2015). Since none of the datasets have a standard develop- ment set, we randomly selected 10% ... See full document

... CNN model. Shrestha et al. (2017) propose a convolutional neural network formulation for AA tasks (detailed in Section 3). They report state- of-the-art performance on a corpus of Twitter data (Schwartz et al., ... See full document

... al., 2006). These datasets represent realistic scenar- ios of AA of user-generated texts with many can- didate authors. For example, Chaski (2005) notes a case where an employee who was terminated for ... See full document

... Authorship attribution research can be broadly categorized in two ...set. Data compression algorithms are best alternative approach for authorship attribution compared with the text ... See full document

... to many design choices the user has to decide ...of data, in-sample ...for authorship detection ...treat authorship attribution as an anomaly detection problem where author regions are ... See full document

... Table 10: 3-author C-FWAA on Chinese blogs Comparing the C-FWAA accuracy on three genres, we can see that function words are quite effective in distinguish writers in all three genres. It is the most effective in novels, ... See full document

... thorship attribution only in a limited study that con- sidered just stopwords (Rajkumar et ...lose authorship-related information when em- ploying LDA, even though the dimensionality of the document ... See full document

... from authors with a putatively distinctive ...of Data-Oriented Parsing (Scha, 1990), which hypothesizes that language production and compre- hension exploits an inventory of fragments from pre- vious ... See full document

... Starting from Mendenhall (1887) AA is one of the oldest computational linguistics problems, which is especially highly topical nowadays. For a long time in the past the main AA applica- tions were restricted to the ... See full document

... For each dataset, we split the documents into training and test sets. Previous studies (Stamatatos et al., 1999) have observed that having unequal number of words per author in the training set leads to poor performance ... See full document

... Our model performs slightly below the SVM at highest sample sizes, and significantly below at some smaller sizes. This is somewhat expected since the SVM weights differ- ent features in order to differentiate the ... See full document

... We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes exten- sions to structural correspondence learning needed to make it appropriate ... See full document

... In Markov’s paper (1916) we find the first application of the idea of Markov chain, used in many fields today e.g. Speech recognition. We consider the straightforward measure i.e. the letters used in texts. We ... See full document