Learning Affect with Distributional Semantic Models

The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data

Name agreement in aphasia

Background: Images are essential materials for assessment and rehabilitation in aphasia. Psycholinguistic research has identified name agreement (the degree to which different people agree on a particular name for a particular image) to be a strong predictor of picture naming in healthy individuals in a wide variety of languages. Despite its significance in naming performance and its impact across linguistic families, studies investigating the effects of name agreement in neuropsychological populations are limited. Determining the impact of name agreement in neuropsychological populations can inform us about lexical processing, which in turn can aid in development of improved assessment and rehabilitation materials. Aims: To compare the naming accuracy and error profile in naming high versus low name agreement (HighNA and LowNA) images in people with aphasia (PWA) and in healthy Adults (HA). Methods & Procedures: Participants were 10 PWA and 21 age and gender-matched HA. Stimuli were black-and-white line drawings of 50 HighNA images (e.g., acorn, bell) and 50 LowNA images (e.g., jacket, mitten). The image sets were closely matched on a range of image and lexical variables. Participants were instructed to name the drawings using single words. Responses were coded into exclusive categories: correct, hesitations, Alternate names, visual errors, semantic errors and omissions. Outcomes and Results: HighNA images were named more accurately than LowNA images; the HA group had higher accuracy than the PWA group; there was a significant interaction in which the name agreement effect was stronger in HA than in PWA. In individual analyses, 7 of 10 PWA participants showed the group pattern of higher accuracies for HighNA, whilst 3 PWA did not. HighNA and LowNA images gave rise to more alternate names in HA than in PWA. There were also fewer visual errors, and more omissions, in PWA than in HA, but only for LowNA items. Conclusions: Name agreement produced measurable differences in naming accuracy for both HA and PWA. PWA shows a reduced effect of name agreement and exhibit a different pattern of errors, compared to healthy controls. We speculate that in picture naming tasks, lower name agreement increases competitive lexical selection, which is difficult for PWA to resolve. In preparation of clinical materials, we need to be mindful of image properties. Future research should replicate our findings in a larger population, and a broader range of pathologies, as well as determine the executive mechanisms underpinning name agreement effects

Process-Oriented Profiling of Speech Sound Disorders

The differentiation between subtypes of speech sound disorder (SSD) and the involvement of possible underlying deficits is part of ongoing research and debate. The present study adopted a data-driven approach and aimed to identify and describe deficits and subgroups within a sample of 150 four to seven-year-old Dutch children with SSD. Data collection comprised a broad test battery including the Computer Articulation Instrument (CAI). Its tasks Picture Naming (PN), NonWord Imitation (NWI), Word and NonWord Repetition (WR; NWR) and Maximum Repetition Rate (MRR) each render a variety of parameters (e.g., percentage of consonants correct) that together provide a profile of strengths and weaknesses of different processes involved in speech production. Principal Component Analysis on the CAI parameters revealed three speech domains: (1) all PN parameters plus three parameters of NWI; (2) the remaining parameters of NWI plus WR and NWR; (3) MRR. A subsequent cluster analysis revealed three subgroups, which differed significantly on intelligibility, receptive vocabulary, and auditory discrimination but not on age, gender and SLPs diagnosis. The clusters could be typified as three specific profiles: (1) phonological deficit; (2) phonological deficit with motoric deficit; (3) severe phonological and motoric deficit. These results indicate that there are different profiles of SSD, which cover a spectrum of degrees of involvement of different underlying problems

Semantic representations of English verbs and their influence on psycholinguistic performance in healthy and language-impaired speakers

PhD ThesisBackground – English verbs are linguistically more complex than nouns and this has contributed to the dearth of in-depth investigation into similarities and differences between their representations within semantic memory and subsequent implications for language processing. However, recent theoretical accounts have argued that verbs and nouns are represented within a unitary semantic system. Aims – This thesis investigates the semantic representations of English verbs with particular attention to how verbs are inter-related as a consequence of semantic similarity. This is achieved through a series of psycholinguistic experiments with healthy adult speakers and an intervention study with adults with aphasia (i.e. acquired communication impairment). Throughout the thesis, comparisons are made to the semantic representations of nouns either directly (i.e. through parallel experimentation) or indirectly (i.e. through the existing literature). Methods – The experiments conducted with healthy adult speakers included: (1) category listing of verbs; (2) typicality rating of verbs within categories; (3) similarity rating of verb pairs; (4) an analysis of verbs’ semantic features; (5) category verification of verbs; and (6) semantically primed picture naming of actions. The intervention study carried out with adults with aphasia compared patterns of improvement in verb and noun retrieval following a semantically-based therapy task. Results and discussion – The results of the experiments shed light on the nature of semantic representations of verbs, in particular, in relation to the similarity between the semantic representations of verbs and those of nouns and also where they differ. These insights are considered in terms of how they provide evidence for or against a unitary semantic system for verbs’ and nouns’ semantic representations and parallel mechanisms for accessing these representations. Two themes emerged in terms of future research potential: (1) the influence of polysemy on speaker’s performance in psycholinguistic tasks; and (2) the nature and influence of typicality within categories/cluster of verbs

Automatic Detection of Dementia and related Affective Disorders through Processing of Speech and Language

In 2019, dementia is has become a trillion dollar disorder. Alzheimer’s disease (AD) is a type of dementia in which the main observable symptom is a decline in cognitive functions, notably memory, as well as language and problem-solving. Experts agree that early detection is crucial to effectively develop and apply interventions and treatments, underlining the need for effective and pervasive assessment and screening tools. The goal of this thesis is to explores how computational techniques can be used to process speech and language samples produced by patients suffering from dementia or related affective disorders, to the end of automatically detecting them in large populations us- ing machine learning models. A strong focus is laid on the detection of early stage dementia (MCI), as most clinical trials today focus on intervention at this level. To this end, novel automatic and semi-automatic analysis schemes for a speech-based cogni- tive task, i.e., verbal fluency, are explored and evaluated to be an appropriate screening task. Due to a lack of available patient data in most languages, world-first multilingual approaches to detecting dementia are introduced in this thesis. Results are encouraging and clear benefits on a small French dataset become visible. Lastly, the task of detecting these people with dementia who also suffer from an affective disorder called apathy is explored. Since they are more likely to convert into later stage of dementia faster, it is crucial to identify them. These are the fist experiments that consider this task us- ing solely speech and language as inputs. Results are again encouraging, both using only speech or language data elicited using emotional questions. Overall, strong results encourage further research in establishing speech-based biomarkers for early detection and monitoring of these disorders to better patients’ lives.Im Jahr 2019 ist Demenz zu einer Billionen-Dollar-Krankheit geworden. Die Alzheimer- Krankheit (AD) ist eine Form der Demenz, bei der das Hauptsymptom eine Abnahme der kognitiven Funktionen ist, insbesondere des Gedächtnisses sowie der Sprache und des Problemlösungsvermögens. Experten sind sich einig, dass eine frühzeitige Erkennung entscheidend für die effektive Entwicklung und Anwendung von Interventionen und Behandlungen ist, was den Bedarf an effektiven und durchgängigen Bewertungsund Screening-Tools unterstreicht. Das Ziel dieser Arbeit ist es zu erforschen, wie computergest ützte Techniken eingesetzt werden können, um Sprach- und Sprechproben von Patienten, die an Demenz oder verwandten affektiven Störungen leiden, zu verarbeiten, mit dem Ziel, diese in großen Populationen mit Hilfe von maschinellen Lernmodellen automatisch zu erkennen. Ein starker Fokus liegt auf der Erkennung von Demenz im Frühstadium (MCI), da sich die meisten klinischen Studien heute auf eine Intervention auf dieser Ebene konzentrieren. Zu diesem Zweck werden neuartige automatische und halbautomatische Analyseschemata für eine sprachbasierte kognitive Aufgabe, d.h. die verbale Geläufigkeit, erforscht und als geeignete Screening-Aufgabe bewertet. Aufgrund des Mangels an verfügbaren Patientendaten in den meisten Sprachen werden in dieser Arbeit weltweit erstmalig mehrsprachige Ansätze zur Erkennung von Demenz vorgestellt. Die Ergebnisse sind ermutigend und es werden deutliche Vorteile an einem kleinen französischen Datensatz sichtbar. Schließlich wird die Aufgabe untersucht, jene Menschen mit Demenz zu erkennen, die auch an einer affektiven Störung namens Apathie leiden. Da sie mit größerer Wahrscheinlichkeit schneller in ein späteres Stadium der Demenz übergehen, ist es entscheidend, sie zu identifizieren. Dies sind die ersten Experimente, die diese Aufgabe unter ausschließlicher Verwendung von Sprache und Sprache als Input betrachten. Die Ergebnisse sind wieder ermutigend, sowohl bei der Verwendung von reiner Sprache als auch bei der Verwendung von Sprachdaten, die durch emotionale Fragen ausgelöst werden. Insgesamt sind die Ergebnisse sehr ermutigend und ermutigen zu weiterer Forschung, um sprachbasierte Biomarker für die Früherkennung und Überwachung dieser Erkrankungen zu etablieren und so das Leben der Patienten zu verbessern