How to blend language and ICT in the didactics of scientific translation

Este capítulo presenta una fusión de la lingüistica aplicada a la traducción científica (análisis del discurso y metadiscurso) y la utilización de las nuevas tecnologías (memorias de traducción, bancos terminológicos). A través del análisis metadiscursivo y la identificación de marcadores en español e inglés, los alumnos crean su propia memoria de traducción y su banco terminológico.This chaper proposes a blend of applied linguistics to scientific translation (discourse and metadiscourse analysis) and some of the new technologies for translation (translation memory and lexical data bank). Through the analysis of metadiscourse in given scientific texts, translators-to-be create their own translation memories and lexical data banks

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology

The Genitive Ratio and its Applications

The genitive ratio (GR) is a novel method of classifying nouns as animate, concrete or abstract. English has two genitive (possessive) constructions: possessive-s (the boy's head) and possessive-of (the head of the boy). There is compelling evidence that preference for possessive-s is strongly influenced by the possessor's animacy. A corpus analysis that counts each genitive construction in three conditions (definite, indefinite and no article) confirms that occurrences of possessive-s decline as the animacy hierarchy progresses from animate through concrete to abstract. A computer program (Animyser) is developed to obtain results-counts from phrase-searches of Wikipedia that provide multiple genitive ratios for any target noun. Key ratios are identified and algorithms developed, with specific applications achieving classification accuracies of over 80%. The algorithms, based on logistic regression, produce a score of relative animacy that can be applied to individual nouns or to texts. The genitive ratio is a tool with potential applications in any research domain where the relative animacy of language might be significant. Three such applications exemplify that. Combining GR analysis with other factors might enhance established co-reference (anaphora) resolution algorithms. In sentences formed from pairings of animate with concrete or abstract nouns, the animate noun is usually salient, more likely to be the grammatical subject or thematic agent, and to co-refer with a succeeding pronoun or noun-phrase. Two experiments, online sentence production and corpus-based, demonstrate that the GR algorithm reliably predicts the salient noun. Replication of the online experiment in Italian suggests that the GR might be applied to other languages by using English as a 'bridge'. In a mental health context, studies have indicated that Alzheimer's patients' language becomes progressively more concrete; depressed patients' language more abstract. Analysis of sample texts suggests that the GR might monitor the prognosis of both illnesses, facilitating timely clinical interventions

Exploration of novel biomarkers in frontotemporal lobar degeneration

Frontotemporal lobar degeneration is a neurodegenerative disorder characterised by progressive deterioration of frontal and anterior temporal lobes of the brain. It can be divided into a number of distinct clinical syndromes including semantic variant of primary progressive aphasia, non-fluent variant of primary progressive aphasia, progressive supranuclear palsy, corticobasal syndrome and behavioural variant frontotemporal dementia. Currently, there are no available treatments for these clinical syndromes. Previous efforts to investigate and develop targeted disease modifying therapies for these conditions have been hampered by a lack of biomarkers that would aid in diagnosis and assessment of disease progression. In this work, I have focused on identifying and examining potential biomarkers across frontotemporal lobar degeneration syndromes. Chapter 1 provides an overview of frontotemporal lobar degeneration syndromes covered in this thesis. In Chapter 2, I present the experimental methods and techniques used in subsequent experiments. Chapter 3 presents the development of a novel combination of existing methods to detect changes in written text output of an individual with semantic variant of primary progressive aphasia. In Chapter 4, I explored whether a right-sided clinical phenotype, defined according to imaging characteristics, exists for non-fluent variant of primary progressive aphasia. Chapter 5 demonstrates the utility of midbrain-pons ratio and magnetic resonance parkinsonism index in distinguishing different histopathological subtypes found in frontotemporal lobar degeneration syndromes – PSP-tau, CBD-tau and TDP-43 – from the control group, those with Alzheimer’s Disease, and from each other. In Chapter 6, I attempted to examine the prevalence and the nature of sleep disturbance in behavioural variant frontotemporal dementia and how this may differ from those with amnestic Alzheimer’s Disease

Unveiling Key Features: A Comparative Study of Machine Learning Models for Alzheimer\u27s Detection

This thesis rigorously evaluates the application of an array of natural language processing (NLP) techniques and machine learning models to identify linguistic signatures indicative of dementia, as sourced from the DementiaBank Pitt corpus. Utilizing a binary classification paradigm, this study meticulously integrates sophisticated embedding methods—including Doc2Vec, Word2Vec, GloVe, and BERT—with traditional machine learning algorithms such as Random Forest, Multinomial Naïve Bayes, ADA boost, KNN classifier, and Logistic Regression, alongside deep learning architectures like LSTM, Bi-LSTM, and CNN-LSTM. The efficacy of these methodologies is evaluated based on their capacity to differentiate between transcribed speech impacted by dementia and that from control subjects. To enhance interpretability, this research also employs feature importance analysis through LIME, SHAP, permutation importance, and integrated gradients, shedding light on the variables most instrumental in driving model predictions. The results of this comprehensive analysis not only illuminate the robust potential of these combined NLP and machine learning approaches in the context of medical screening but also contribute additional valuable insights to the field of NLP and dementia screening specifically

The impact of centre based respite on occupational performance for people with dementia

Dementia affects occupational performance by restricting one\u27s ability to carry out valued roles and activities. Centre based respite has been described as one intervention to maintain or improve occupational performance and prevent or delay institutionalisation for people with dementia. The purpose of this study was to explore the impact of centre based respite on occupational performance components as outlined by the Occupational Performance Model (Australia) for people with dementia. Five spousal caregivers, recruited through Alzheimer\u27s Australia WA, participated in semi-structured interviews. Data was analysed using framework analysis techniques. Findings revealed centre based respite attendance can positively influence marital relationships, mood, socialisation and sleep habits for people with dementia. Future research is required that examines the efficacy of interventions that focus on occupational performance components for people with dementia within a centre based respite environment