Conversation based group therapy for people with aphasia (PWA) has been shown to be an efficacious treatment approach benefitting participants’ communication abilities and quality of life. However, little is known re...
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Conversation based group therapy for people with aphasia (PWA) has been shown to be an efficacious treatment approach benefitting participants’ communication abilities and quality of life. However, little is known regarding the practices facilitators should follow to best support the process of communication rehabilitation. Researchers interested in studying this treatment approach face limitations due to the available methodologies. The current study aimed to fill this gap by beginning to expand the established discourse structure analysis speech function network developed for investigation of casual conversation for use on conversations including PWA. This study focused on repair sequences, given the ubiquity of this type of segment in group conversation therapy. Further, this study oriented towards providing preliminary information about the reliability of the novel repair-focused coding framework. During the first phase, video recordings of four group therapy sessions were analyzed to operationalize a comprehensive class of speech functions that represent the behaviors enacted in sequences that orient to understanding ambiguous productions by PWA. This formed the repair speech function class. Next, two student research assistants were trained to apply the repair speech function framework to excerpts from group conversation therapy. Through the iterative and collaborative process employed in this study, 17 novel speech functions were operationalized. Inter-rater reliability of trained coders applying the repair speech function class was variable across coders but generally showed substantial agreement. The results of this study suggest that the development of a comprehensive network to systematically study group therapy sessions is feasible, though continued research is needed to explicate an optimal framework.
discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text summarization and text categorization. However, for the Chinese news domain, the discoursestructure analysi...
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ISBN:
(纸本)9798350397444
discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text summarization and text categorization. However, for the Chinese news domain, the discourse structure analysis system is still immature due to the limitation of the lack of expert-annotated datasets. In this paper, we present CNA, a Chinese news corpus containing 1155 news articles annotated by human experts, which covers four domains and four news media sources. Next, we implement several text classification methods as baselines. Experimental results demonstrate that document-level method can achieve a better performance, and we further propose a document-level neural network model with multiple sentence features which achieves the state-of-the-art performance. In the end, we analyze the content type distribution of each sentence in CNA and the prediction errors of our model that occurred on the test set. The codes and dataset will be open-sourced at https://***/gzl98/Chinese discourse Profiling.
In this article, we propose a novel method for generating engaging multi-modal content automatically from text. Rhetorical structure Theory (RST) is used to decompose text into discourse units and to identify rhetoric...
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In this article, we propose a novel method for generating engaging multi-modal content automatically from text. Rhetorical structure Theory (RST) is used to decompose text into discourse units and to identify rhetorical discourse relations between them. Rhetorical relations are then mapped to question-answer pairs in an information preserving way, i.e., the original text and the resulting dialogue convey essentially the same meaning. Finally, the dialogue is "acted out" by two virtual agents. The network of dialogue structures automatically built up during this process, called DialogueNet, can be reused for other purposes, such as personalization or question-answering.
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