Contemporary mormon literature: Phyllis Barber's writing

612 p.

A High Degree of LINE-1 Hypomethylation Is a Unique Feature of Early-Onset Colorectal Cancer

12 p.

Keep an "eye" on the text: The Use of Self-mention in the Academic Writing of Experts

One of the most controversial inquiries in academic writing is whether it is admissible to use first person pronouns in a scientific paper or not. Many professors discourage their students from using them, rather favoring a more passive tone, and thus causing novices to avoid inserting themselves into their texts in an expert-like manner. Abundant research, however, has recently attested that negotiation of identity is plausible in academic prose, and there is no need for a paper to be void of an authorial identity. Because in the course of the English Studies Degree we have received opposing prompts in the use of I, the aim of this dissertation is to throw some light upon this vexed issue. To this end, I compiled a corpus of 16 Research Articles (RAs) that comprises two sub-corpora, one featuring Linguistics RAs and the other one Literature RAs, and each, in turn, consists of articles written by American and British authors. I then searched for real occurrences of I, me, my, mine, we, us, our and ours, and studied their frequency, rhetorical functions and distribution along each paper. The results obtained certainly show that academic writing is no longer the faceless prose that it used to be, for I is highly used in both disciplines and varieties of English. Concerning functions, the most typically used roles were the use of I to take credit for the writer’s research process, and also those involving plural forms. With respect to the spatial disposition, all sections welcomed first person pronouns, but the Method and the Results/Discussion sections seem to stimulate their appearance. On the basis of these findings, I suggest that an L2 writing pedagogy that is mindful not only of the language proficiency, but also of the students’ own identity may have a beneficial effect on the composition of their texts.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.