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Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles
Production of news content is growing at an astonishing rate.
Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
The large volume of text in electronic healthcare records often remains underused due to a lack of methodologies to extract interpretable content.
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
Electronic Healthcare records contain large volumes of unstructured data in different forms.
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning
The explosion in the amount of news and journalistic content being generated across the globe, coupled with extended and instantaneous access to information through online media, makes it difficult and time-consuming to monitor news developments and opinion formation in real time.
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology
Electronic Healthcare Records contain large volumes of unstructured data, including extensive free text.
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