Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Linguistic Annotation Framework to Study Interactions in Multilingual Healthcare Conversational Forums
إطار شرح لغوي لدراسة التفاعلات في منتديات محادثة الرعاية الصحية متعددة اللغات
643
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في السنوات الأخيرة، اكتسب الرعاية الصحية الرقمية عن بعد باستخدام الدردشات عبر الإنترنت زخما، خاصة في الجنوب العالمي. على الرغم من أن العمل السابق قد درس أنماط التفاعل في المنتديات عبر الإنترنت (الصحة)، مثل Talklife، Reddit و Facebook، كان هناك عمل محدود في فهم التفاعلات في مجتمع صغير مقبول من الرسل الفوري. في هذه الورقة، نقترح إطار شرح لغوي لتسهيل تحليل مجموعات WhatsApp التي تركز على الصحة. الهدف الأساسي للإطار هو فهم العلاقات الشخصية بين مؤيديه الأقران من أجل المساعدة في تطوير حلول NLP لرعاية المرضى عن بعد وتقليل عبء مقدمي الرعاية الصحية المرهقين. يتكون إطارنا من تصنيف دعم الأقران من النظير الدقيق ووضع علامات معنويات على مستوى الرسائل. بالإضافة إلى ذلك، نظرا لانتشار خلط التعليمات البرمجية في هذه المجموعات، فإننا ندمج التعليقات التوضيحية لغة مستوى Word. نستخدم الإطار المقترح لدراسة مجموعتين WhatsApp في كينيا للشباب الذين يعيشون مع فيروس نقص المناعة البشرية، ويسهل من قبل مزود للرعاية الصحية.
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South. Though prior work has studied interaction patterns in online (health) forums, such as TalkLife, Reddit and Facebook, there has been limited work in understanding interactions in small, close-knit community of instant messengers. In this paper, we propose a linguistic annotation framework to facilitate analysis of health-focused WhatsApp groups. The primary aim of the framework is to understand interpersonal relationships among peer supporters in order to help develop NLP solutions for remote patient care and reduce burden of overworked healthcare providers. Our framework consists of fine-grained peer support categorization and message-level sentiment tagging. Additionally, due to the prevalence of code-mixing in such groups, we incorporate word-level language annotations. We use the proposed framework to study two WhatsApp groups in Kenya for youth living with HIV, facilitated by a healthcare provider.
References used
https://aclanthology.org/
rate research
Read More
Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. T
o address this urgent problem, we developed a system which allows healthcare professionals to translate sentences that are frequently used in the diagnosis and treatment of COVID-19 into Sign Language of the Netherlands (NGT). Translations are displayed by means of videos and avatar animations. The architecture of the system is such that it could be extended to other applications and other sign languages in a relatively straightforward way.
This paper presents several challenges faced when annotating Turkish treebanks in accordance with the Universal Dependencies (UD) guidelines and proposes solutions to address them. Most of these challenges stem from the lack of adequate support in th
e UD framework to accurately represent null morphemes and complex derivations, which results in a significant loss of information for Turkish. This loss negatively impacts the tools that are developed based on these treebanks. We raised and discussed these issues within the community on the official UD portal. This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD. This work aims to contribute to the representation of Turkish and other agglutinative languages in UD-based treebanks, which in turn aids to develop more accurately annotated datasets for such languages.
In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related ima
ges annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
There has been significant progress in dialogue systems research. However, dialogue systems research in the healthcare domain is still in its infancy. In this paper, we analyse recent studies and outline three building blocks of a task-oriented dialo
gue system in the healthcare domain: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centric evaluations. To this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowd-sourcing.
Technologies for enhancing well-being, healthcare vigilance and monitoring are on the rise. However, despite patient interest, such technologies suffer from low adoption. One hypothesis for this limited adoption is loss of human interaction that is c
entral to doctor-patient encounters. In this paper we seek to address this limitation via a conversational agent that adopts one aspect of in-person doctor-patient interactions: A human avatar to facilitate medical grounded question answering. This is akin to the in-person scenario where the doctor may point to the human body or the patient may point to their own body to express their conditions. Additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events. Both the avatar, and the multiple interaction modes could help improve adherence. We present a high level overview of the design of our agent, Marie Bot Wellbeing. We also report implementation details of our early prototype , and present preliminary results.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
