Natural language processing techniques for studying language in pathological ageing: A scoping review

Gagliardi, Gloria

doi:10.1111/1460-6984.12870

Cited by 4 publications

(3 citation statements)

References 111 publications

(153 reference statements)

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“…(Nedergaard et al, 2022). They involve the representation of dialogue, speech, or other forms of linguistic expression (Gagliardi, 2024;X. Li & Qu, 2024).…”

Section: Verbal Processmentioning

confidence: 99%

Learning the Meaning of Song Lyrics through the Elements of Ideational Metafunction: A Case on Systemic Functional Linguistics

Silalahi,

Purba,

Manurung

et al. 2024

anglophile

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The focus of the analysis is to identify and describe the various types of processes within the Experiential Function in the song's lyrics. Data source for this study is the song's lyrics, obtained from reliable sources such as the artist's official YouTube channel, Google, and various music applications. The lyrics are transcribed and used for identifying and categorizing the experiential processes found in the song. The data analysis method used in this research is qualitative analysis. It involves analyzing each clause in the lyrics and classifying them based on the Experiential Function's categories. The Experiential Function analysis helps to understand how the songwriter expresses their experiences, emotions, and perceptions through language in the song. This analysis of the Experiential Function in the song "A Year Ago" provides a comprehensive understanding of the songwriter's expressions of experiences and emotions, contributing to a deeper appreciation and interpretation of the song's lyrics. Keywords: Ideational Metafunction, Lyrics Songs, Systemic Functional Linguistics

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“…(Nedergaard et al, 2022). They involve the representation of dialogue, speech, or other forms of linguistic expression (Gagliardi, 2024;X. Li & Qu, 2024).…”

Section: Verbal Processmentioning

confidence: 99%

Learning the Meaning of Song Lyrics through the Elements of Ideational Metafunction: A Case on Systemic Functional Linguistics

Silalahi,

Purba,

Manurung

et al. 2024

anglophile

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“…However, it is crucial for clinicians to acknowledge and be ready to employ the assessments most suitable for the person. This involves taking into account the person's cultural, educational and linguistic requirements (Arvanitakis et al., 2019; Gagliardi, 2023; Galvin & Sadowsky, 2012). In instances where necessary, certain assessments have been translated into various languages, condensed or adjusted in terms of scoring classifications to accommodate varying educational levels (World Health Organization, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…Cognitive decline arises due to the damage in the brain cells. Individuals experiencing mild cognitive impairment (MCI) typically exhibit (a) a personal perception of cognitive issues validated by an informant, (b) maintained overall cognitive capabilities, (c) challenges in one or more cognitive areas such as memory, attention‐executive function visuospatial skills, and/or language, and (d) mostly regular performance in daily activities (Gagliardi, 2023).…”

Section: Introductionmentioning

confidence: 99%

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture

PL,

2023

Intl J Lang & Comm Disor

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BackgroundDementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking.AimsThis research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall.Methods & ProceduresThis is a cross‐sectional, online, self‐administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence‐Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi‐QA‐MPNet (Multi‐Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence‐Transformer.Outcomes & ResultsThe results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task.Conclusions & ImplicationsThis finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images.WHAT THIS PAPER ADDSWhat is already known on this subject It is already known that speech‐ and language‐based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech‐ and language‐based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations.What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care.What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.

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Automatic Diagnosis of Medical Conditions Using Deep Learning With Symptom2Vec

Kim,

Joe

2023

IEEE Access

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In this paper, a medical examination algorithm is proposed that can collect users' symptoms and automatically issue a diagnosis. The proposed algorithm makes use of "Symptom2Vec" and the "analysis model of responses on self-diagnosis questions" (AMoRSD) for real-time interviews with users. Symptom2Vec can learn about the relationship between terms related to the symptoms and disease, and establish questioning criteria to be used in patient health checkups, as well as general appropriate followup questions based on patient symptomology. AMoRSD analyzes the patient's emotional expressions and responses to self-diagnostic questions, classifying them into "Sick," "Not Sick," and "Neutral" categories based on patterns. Compared to traditional models, Symptom2Vec earned the highest mean symptom similarity score of 0.983. Furthermore, compared to other models that only learn from patient responses, AMoRSD demonstrates an area under curves (AUC) of 0.99%, indicating that jointly learning the relationship between emotions and patient responses improves the accuracy of user response classification. The combined algorithm of Symptom2Vec and AMoRSD enhances the efficiency and accuracy of user symptom collection and appropriate diagnosis generation. The data were collected from reliable medical sources such as WebMD Dictionary, NHS inform, Snomed Ct, and Cleveland Clinic, encompassing 526 disease names and 2078 symptoms. Additional data were obtained for AMoRSD, focusing on conversations within a hospital context, and effectively trained and evaluated the model using diverse and representative datasets. This research addresses the importance of medical history-taking and contributes to the field by providing a robust framework for real-time symptom-based diagnosis in clinical environments.

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Natural language processing techniques for studying language in pathological ageing: A scoping review

Cited by 4 publications

References 111 publications

Learning the Meaning of Song Lyrics through the Elements of Ideational Metafunction: A Case on Systemic Functional Linguistics

Learning the Meaning of Song Lyrics through the Elements of Ideational Metafunction: A Case on Systemic Functional Linguistics

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture

Automatic Diagnosis of Medical Conditions Using Deep Learning With Symptom2Vec

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