[PDF] Top 20 Tri Training for Authorship Attribution with Limited Training Data

... This data set contains the IMDb reviews in May ...labeled data for training, 79% are used as unlabeled data, and the rest 20% are used for ...raw data, and use the Stanford PCFG parser ... See full document

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

... The AA methods can be useful in applied areas such as law and journalism where the identification of the true author of a piece of text (such as a ransom note) may be able to save lives or help prosecute offenders. One ... See full document

... We first run the classification for the English and Span- ish datasets separately (as a classical authorship attribution task). For both datasets the best accuracy was achieved by linear SVM with ... See full document

... state-of-the-art authorship attribution performance on this feature set; that is, when comparing methods without any further feature engineering, Token SVM is expected to yield good performance with minimal ... See full document

... Boutwell (2011) used a naive Bayes classifier with character n-gram features. She experimented with 50 authors and two training size values (120 and 230). She also provided a set of experiments that studied the ... See full document

... The use of orthographical features in the attri- bution task was studied in Abbasi and Chen (2008). The features included characters, charac- ters bigrams and trigrams, punctuation and spe- cial characters, as well as ... See full document

... the data into training (90%) and test (10%) sets and conduct a 10-fold cross-validation on the training set for model ...all training instances are labeled and only holdout and test articles ... 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 sending a racist ... See full document

... Authorship attribution research can be broadly categorized in two ...and training set. Data compression algorithms are best alternative approach for authorship attribution ... 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

... We present a method of authorship attribution and stylometry that exploits hierarchical infor- mation in phrase-structures. Contrary to much previous work in stylometry, we focus on con- tent words rather ... 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

... A combination of the two approaches ap- pears in (Shrestha et al., 2017) where a text is classified based on a sequence of input char- acters. The method uses straightforward se- quences of characters and their n-grams ... See full document

... unlabeled data, ...the training dataset containing texts of known au- thors is available and can be used to create the model able to predict the authorship of unknown texts from the same closed-set ... See full document

... capture data is through the use of a support vector machine ...some training dataset in such a way that all or most instances of one class fall in the same partition of that ...the data that it can ... See full document

... In a first step, we created a baseline by computing the accuracies achieved for direct authorship at- tribution, i.e., for finding the correct author with- out any reduction. For this, we utilized the pro- posed ... See full document

... When applied to 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 8.1%. Argamon et al. (2003) also report on ... See full document

... Authorship attribution research can be broadly categorized in two ...between training and test document shows the similarity between two ...the training set of an author, while a large ... See full document

... B&L apply this model to a sentence order- ing task, where the more coherent option, as ev- idenced by its transition probabilities, was cho- sen. In authorship attribution, texts are however assumed to ... See full document