An analysis of authorship attribution: Identifying linguistic variables in oral and written discourse

This dissertation goes into the new field from applied linguistics called forensic linguistics, which studies the language as an evidence for criminal cases. There are many subfields within forensic linguistics, however, this study belongs to authorship attribution analysis, where the authorship of a text is attributed to an author through an exhaustive linguistic analysis. Within this field, this study analyzes the morphosyntactic and discursive-pragmatic variables that remain constant in the intra-variation or personal style of a speaker in the oral and written discourse, and at the same time have a high difference rate in the interspeaker variation, or from one speaker to another. The theoretical base of this study is the term used by professor Maria Teresa Turell called “idiolectal style”. This term establishes that the idiosyncratic choices that the speaker makes from the language build a style for each speaker that is constant in the intravariation of the speaker’s discourse. This study comes as a consequence of the problem appeared in authorship attribution analysis, where the absence of some known texts impedes the analysis for the attribution of the authorship of an uknown text. Thus, through a methodology based on qualitative analysis, where the variables are studied exhaustively, and on quantitative analysis, where the findings from qualitative analysis are statistically studied, some conclusions on the evidence of such variables in both oral and written discourses will be drawn. The results of this analysis will lead to further implications on deeper analyses where larger amount of data can be used.

Principal Component Analysis of Results Obtained from Finite-Difference Time-Domain Algorithms

Finite-Differences Time-Domain (FDTD) algorithms are well established tools of computational electromagnetism. Because of their practical implementation as computer codes, they are affected by many numerical artefact and noise. In order to obtain better results we propose using Principal Component Analysis (PCA) based on multivariate statistical techniques. The PCA has been successfully used for the analysis of noise and spatial temporal structure in a sequence of images. It allows a straightforward discrimination between the numerical noise and the actual electromagnetic variables, and the quantitative estimation of their respective contributions. Besides, The GDTD results can be filtered to clean the effect of the noise. In this contribution we will show how the method can be applied to several FDTD simulations: the propagation of a pulse in vacuum, the analysis of two-dimensional photonic crystals. In this last case, PCA has revealed hidden electromagnetic structures related to actual modes of the photonic crystal.