1,872 research outputs found
Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis
Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach
Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation
This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach.
The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
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Computational Models of Argument Structure and Argument Quality for Understanding Misinformation
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that can find checkworthy information, detect fallacious argumentation of online content, retrieve relevant evidence from authoritative sources and analyze the veracity of claims given the retrieved evidence. The robustness and applicability of these systems depend on the availability of annotated resources to train machine learning models in a supervised fashion, as well as machine learning models that capture patterns beyond domain-specific lexical clues or genre-specific stylistic insights. In this thesis, we investigate the role of models for argument structure and argument quality in improving tasks relevant to fact-checking and furthering our understanding of misinformation and disinformation. We contribute to argumentation mining, misinformation detection, and fact-checking by releasing multiple annotated datasets, developing unified models across datasets and task formulations, and analyzing the vulnerabilities of such models in adversarial settings.
We start by studying the argument structure's role in two downstream tasks related to fact-checking. As it is essential to differentiate factual knowledge from opinionated text, we develop a model for detecting the type of news articles (factual or opinionated) using highly transferable argumentation-based features. We also show the potential of argumentation features to predict the checkworthiness of information in news articles and provide the first multi-layer annotated corpus for argumentation and fact-checking.
We then study qualitative aspects of arguments through models for fallacy recognition. To understand the reasoning behind checkworthiness and the relation of argumentative fallacies to fake content, we develop an annotation scheme of fallacies in fact-checked content and investigate avenues for automating the detection of such fallacies considering single- and multi-dataset training. Using instruction-based prompting, we introduce a unified model for recognizing twenty-eight fallacies across five fallacy datasets. We also use this model to explain the checkworthiness of statements in two domains.
Next, we show our models for end-to-end fact-checking of statements that include finding the relevant evidence document and sentence from a collection of documents and then predicting the veracity of the given statements using the retrieved evidence. We also analyze the robustness of end-to-end fact extraction and verification by generating adversarial statements and addressing areas for improvements for models under adversarial attacks. Finally, we show that evidence-based verification is essential for fine-grained claim verification by modeling the human-provided justifications with the gold veracity labels
Effects of Traumatic Brain Injury on the Intestinal Tract and Gut Microbiome
Traumatic brain injury (TBI) initiates not only complex neurovascular and glial changes within the brain but also pathophysiological responses that extend beyond the central nervous system. The peripheral response to TBI has become an intensive area of research, as these systemic perturbations can induce dysfunction in multiple organ systems. As there are no approved therapeutics for TBI, it is imperative that we investigate the peripheral response to TBI to identify targets for future intervention. Of particular interest is the gastrointestinal (GI) system. Even in the absence of polytrauma, brain-injured individuals are at increased risk of suffering from GI-related morbidity and mortality. Symptoms such as intestinal dysmotility, inflammation, ulceration, and fecal incontinence can drastically diminish quality of life. The GI tract is inhabited by trillions of microbes that have been implicated as modulators of many neurological disorders. Clinical and preclinical studies implicate gut dysbiosis, a pathological imbalance in the normally symbiotic microbiota, as both a consequence of TBI as well as a contributing factor to brain damage.However, our understanding of this interplay is still limited. While relatively little is known about the effects of TBI on the structure and function of the GI tract, prior studies report that experimental TBI induces intestinal barrier dysfunction and morphological changes. To confirm these findings, male C57BL/6J mice underwent a sham control or a controlled cortical impact (CCI) procedure to induce a contusive brain injury, and intestinal permeability was assessed at 4 h, 8 h, 1 d, and 3 d post-injury. An acute, transient increase in permeability was observed at 4 h after CCI. Histological analyses of the ileum and colon at multiple time points from 4 h to 4 wks revealed no overt morphological changes, suggesting that CCI induced a short-lived physiologic dysfunction without major structural alterations to the GI tract. As the microbiome is a modulator of GI physiology, we performed 16s gene sequencing on fecal samples collected prior to and over the first month after CCI or sham injury. Microbial community diversity was assessed using common metrics of alpha and beta diversity. Alpha diversity was lower in the CCI injury group and beta diversity differed among groups, although these effects were not observed in all metrics. Subsequent differential abundance analysis revealed that the phylum Verrucomicrobiota was increased in CCI mice at 1, 2, and 3 d post-injury when compared to sham mice. Subsequent qPCR identified the Verrucomicrobiota species as Akkermansia muciniphila, an obligate anaerobe that resides in and helps regulate the intestinal mucus layer and barrier. To determine whether TBI promotes changes to the GI tract favorable for the proliferation of A. muciniphila, mucus-producing goblet cells and the level of GI hypoxia were evaluated. Goblet cell density in the medial colon was significantly increased at 1 d, while colon hypoxia was significantly increased at 3 d. Taken together, these studies show that CCI induces transient intestinal barrier dysfunction followed by increased goblet cell density and hypoxia in the colon with a concomitant increase in A. muciniphila that may suggest a compensatory response to systemic stress after TBI
Ditransitives in germanic languages. Synchronic and diachronic aspects
This volume brings together twelve empirical studies on ditransitive constructions in Germanic languages and their varieties, past and present. Specifically, the volume includes contributions on a wide variety of Germanic languages, including English, Dutch, and German, but also Danish, Swedish, and Norwegian, as well as lesser-studied ones such as Faroese. While the first part of the volume focuses on diachronic aspects, the second part showcases a variety of synchronic aspects relating to ditransitive patterns. Methodologically, the volume covers both experimental and corpus-based studies. Questions addressed by the papers in the volume are, among others, issues like the cross-linguistic pervasiveness and cognitive reality of factors involved in the choice between different ditransitive constructions, or differences and similarities in the diachronic development of ditransitives. The volume’s broad scope and comparative perspective offers comprehensive insights into well-known phenomena and furthers our understanding of variation across languages of the same family
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems.Comment: 137 page
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