Literary Detective Work on the Computer by Michael P. Oakes

By Michael P. Oakes

Computational linguistics can be utilized to discover mysteries in textual content which aren't regularly visible to visible inspection. for instance, the pc research of writing variety can convey who should be the real writer of a textual content in circumstances of disputed authorship or suspected plagiarism. The theoretical historical past to authorship attribution is gifted in a step-by-step demeanour, and complete reports of the sphere are given in expert components, the writings of William Shakespeare and his contemporaries, and many of the writing types noticeable in spiritual texts. the ultimate bankruptcy seems to be on the growth desktops have made within the decipherment of misplaced languages. This booklet is written for college kids and researchers of normal linguistics, computational and corpus linguistics, and computing device forensics. it is going to motivate destiny researchers to check those themes for themselves, and offers enough info of the tools and assets to get them began.

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Pronoun questions passives nominalisations −1st pers. 17 Correlations close to 1 show that two linguistic features tend to vary together – here texts which have large numbers of nominalisations also tend to have large counts of passives. Correlations close to 0 show that two features vary independently of each other, so a text with the number of nominalisations in a text has little to do with the number of first person pronouns. Although there is no instance of it in this table, a correlation coefficient close to −1 would mean that a high count of one feature in a text would generally be associated with a low count of another, and vice versa.

Factor analysis is a computational technique for discovering such underlying factors or “components” automatically from large numbers of initial features. The factors are ordered, so that the first one to be extracted explains most of the variation in the original data set, which usually arises from the largest set of correlated linguistic features. The second component finds the greatest source of variation from the residual data after the first component has been “extracted” or removed from consideration, then the third component finds the main source of variation after the second component has been extracted and so on.

The line from the origin to x = 12 and 37 38 Literary Detective Work on the Computer y = 8 would follow exactly the same direction, but be four times as long. The length of the line corresponding to the [3, 2] vector is found by the Euclidean distance between the start and end point, in this case √(32 + 22) = √13. Eigenvectors can only be found for square matrices, although not every square matrix has them. If a matrix has eigenvectors, there will be as many as there are rows or columns in this matrix.

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