Support of informal carers for people after a stroke with crowdsourcing and natural language processing

Petr Šaloun, Barbora Cigánková, David Andrešič, Lenka Krhutová

Support of informal carers for people after a stroke with crowdsourcing and natural language processing

Číslo: 3/2021
Periodikum: Acta Electrotechnica et Informatica
DOI: 10.15546/aeei-2021-0013

Klíčová slova: ClassificationText documentsNatural language processingDocuments similarityN-gramsCrowdsourcingWordPressCaretakerStroke

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Anotace: For a long time, both professionals and the lay public showed little interest in informal carers. Yet these people deals with multiple and common issues in their everyday lives. As the population is aging we can observe a change of this attitude. And thanks to the advances in computer science, we can offer them some effective assistance and support by providing necessary information and connecting them with both professional and lay public community.

In this work we describe a project called “Research and development of support networks and information systems for informal carers for persons after stroke” producing an information system visible to public as a web portal. It does not provide just simple a set of information but using means of artificial intelligence, text document classification and crowdsourcing further improving its accuracy, it also provides means of effective visualization and navigation over the content made by most by the community itself and personalized on a level of informal carer’s phase of the care-taking timeline.

In can be beneficial for informal carers as it allows to find a content specific to their current situation. This work describes our approach to classification of text documents and its improvement through crowdsourcing. Its goal is to test text documents classifier based on documents similarity measured by N-grams method and to design evaluation and crowdsourcing-based classification improvement mechanism. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful.