title: Concept-based Text Clustering

authors: Huang, Lan

subjects: clustering

abstract: Thematic organization of text is a natural practice of humans and a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates exploration, search and interpretation of current texts, as well as organization of future ones. Automatic clustering is usually based on words. Text is represented by the words it mentions, and thematic similarity is based on the proportion of words that texts have in common. The resulting bag-of-words model is semantically ambiguous and undesirably orthogonal|it ignores the connections between words. This thesis claims that using concepts as the basis of clustering can significantly improve effectiveness. Concepts are defined as units of knowledge. When organized according to the relations among them, they form a concept system. Two concept systems are used here: WordNet, which focuses on word knowledge, and Wikipedia, which encompasses world knowledge. We investigate a clustering procedure with three components: using concepts to represent text; taking the semantic relations among them into account during clustering; and learning a text similarity measure from concepts and their relations. First, we demonstrate that concepts provide a succinct and informative representation of the themes in text, exemplifying this with the two concept systems. Second, we define methods for utilizing concept relations to enhance clustering by making the representation models more discriminative and extending thematic similarity beyond surface overlap. Third, we present a similarity measure based on concepts and their relations that is learned from a small number of examples, and show that it both predicts similarity consistently with human judgement and improves clustering. The thesis provides strong support for the use of concept-based representations instead of the classic bag-of-words model.

text: Concept-based Text Clustering Thematic organization of text is a natural practice of humans and a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates exploration, search and interpretation of current texts, as well as organization of future ones. Automatic clustering is usually based on words. Text is represented by the words it mentions, and thematic similarity is based on the proportion of words that texts have in common. The resulting bag-of-words model is semantically ambiguous and undesirably orthogonal|it ignores the connections between words. This thesis claims that using concepts as the basis of clustering can significantly improve effectiveness. Concepts are defined as units of knowledge. When organized according to the relations among them, they form a concept system. Two concept systems are used here: WordNet, which focuses on word knowledge, and Wikipedia, which encompasses world knowledge. We investigate a clustering procedure with three components: using concepts to represent text; taking the semantic relations among them into account during clustering; and learning a text similarity measure from concepts and their relations. First, we demonstrate that concepts provide a succinct and informative representation of the themes in text, exemplifying this with the two concept systems. Second, we define methods for utilizing concept relations to enhance clustering by making the representation models more discriminative and extending thematic similarity beyond surface overlap. Third, we present a similarity measure based on concepts and their relations that is learned from a small number of examples, and show that it both predicts similarity consistently with human judgement and improves clustering. The thesis provides strong support for the use of concept-based representations instead of the classic bag-of-words model.

year: 2011

title: Evaluating depression, anxiety, and stress assessment before and during the COVID-19 pandemic using Generalisability Theory

authors: Lightburn, Scott James

subjects: Depression

abstract: The evaluation of depression, anxiety, and stress requires distinguishing between state and trait changes. Otherwise, the accuracy of evaluating the effectiveness of interventions and the reliability and validity of research can be compromised. The COVID-19 pandemic has introduced extraneous variables that have the potential to significantly impact the measurement of affective conditions and the distinction between state or trait symptoms. The Depression Anxiety and Stress Scales (DASS-21) is the only psychometric scale that measures depression, anxiety, and stress, and the overall psychological distress simultaneously. It is widely used and applicable to both clinical and non-clinical populations. However, no study has assessed its ability to distinguish state from trait changes or investigated the impact of the COVID-19 pandemic on its temporal reliability. In this study, Generalisability Theory (G-Theory) was applied to evaluate the DASS-21 under normal and COVID-19 pandemic conditions. Data was collected from two independent samples. The first sample was collected in the United States (n = 115) before the pandemic and the second sample was collected in New Zealand (n = 114) during the pandemic. In both samples the data were collected on three occasions with 2 to 4-week intervals. The total DASS-21 scale showed excellent reliability in measuring enduring symptoms of psychological distress (G = 0.94 to 0.96), and while all DASS-21 subscales demonstrated good reliability in the pre-pandemic US sample, they showed lower temporal reliability in the NZ sample during the pandemic. These findings suggest that the overall DASS-21 reliably measures psychological distress as a trait and its scores can be generalised across different populations and occasions. However, the DASS-21 subscales are not as reliable during emergencies and uncertainty as seen in the COVID-19 pandemic. The overall psychological distress remains stable across individuals and can be reliably measured by the DASS-21, but shifts in depression, anxiety, and stress levels are likely during emergencies and uncertainty.

text: Evaluating depression, anxiety, and stress assessment before and during the COVID-19 pandemic using Generalisability Theory The evaluation of depression, anxiety, and stress requires distinguishing between state and trait changes. Otherwise, the accuracy of evaluating the effectiveness of interventions and the reliability and validity of research can be compromised. The COVID-19 pandemic has introduced extraneous variables that have the potential to significantly impact the measurement of affective conditions and the distinction between state or trait symptoms. The Depression Anxiety and Stress Scales (DASS-21) is the only psychometric scale that measures depression, anxiety, and stress, and the overall psychological distress simultaneously. It is widely used and applicable to both clinical and non-clinical populations. However, no study has assessed its ability to distinguish state from trait changes or investigated the impact of the COVID-19 pandemic on its temporal reliability. In this study, Generalisability Theory (G-Theory) was applied to evaluate the DASS-21 under normal and COVID-19 pandemic conditions. Data was collected from two independent samples. The first sample was collected in the United States (n = 115) before the pandemic and the second sample was collected in New Zealand (n = 114) during the pandemic. In both samples the data were collected on three occasions with 2 to 4-week intervals. The total DASS-21 scale showed excellent reliability in measuring enduring symptoms of psychological distress (G = 0.94 to 0.96), and while all DASS-21 subscales demonstrated good reliability in the pre-pandemic US sample, they showed lower temporal reliability in the NZ sample during the pandemic. These findings suggest that the overall DASS-21 reliably measures psychological distress as a trait and its scores can be generalised across different populations and occasions. However, the DASS-21 subscales are not as reliable during emergencies and uncertainty as seen in the COVID-19 pandemic. The overall psychological distress remains stable across individuals and can be reliably measured by the DASS-21, but shifts in depression, anxiety, and stress levels are likely during emergencies and uncertainty.

year: 2023

title: An evaluation of the Raukawa Health Services Kaumatua Mirimiri Programme

authors: Gregg, Lisa

subjects: Maori

abstract: The Kaumatua Mirimiri Programme was funded as a Service to Improve Access project under a contract which ran from 1st September 2004 to 30th June 2005. Its aim was to provide a “culturally based treatment and recovery programme to restore the health and independence” of people aged 40 years and over (Pinnacle Group Ltd, 2004a, p.1). However, as the service specification in the contract made clear, the programme was not designed be exclusive: it has attracted younger as well as older people, non-Māori as well as Māori, and people seeking help for a wide range of ailments and pain.

text: An evaluation of the Raukawa Health Services Kaumatua Mirimiri Programme The Kaumatua Mirimiri Programme was funded as a Service to Improve Access project under a contract which ran from 1st September 2004 to 30th June 2005. Its aim was to provide a “culturally based treatment and recovery programme to restore the health and independence” of people aged 40 years and over (Pinnacle Group Ltd, 2004a, p.1). However, as the service specification in the contract made clear, the programme was not designed be exclusive: it has attracted younger as well as older people, non-Māori as well as Māori, and people seeking help for a wide range of ailments and pain.

year: 2006-03