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- W4382201771 abstract "Classical tasks of a librarian, such as screening and categorizing new documents based on their content, are increasingly replaced by search engines or through the use of cataloging software. A first overview of a corpus topical orientation can be achieved by combining graph-based search engines and clustering methods. Existing classical clustering methods, however, often require an a priori specification of the desired number of clusters to be output and do not consider term relationships in graphs, which is deficient from a practical point of view. Therefore, fully unsupervised graph-based clustering approaches at the term level offer new possibilities that mitigate these shortcomings. Within this work, a set of novel graph-based clustering algorithms have been developed. The hierarchical clustering algorithm (HCA) forms term hierarchies by iteratively isolating nodes of a given co-occurrence graph based on the evaluation of the edge weight between the nodes. Based on the co-occurrence graph inherent relationships of terms, a new graph is built agglomerative forming individual term clusters of related terms. The feasibility of the outlined methods for text analysis is shown." @default.
- W4382201771 created "2023-06-28" @default.
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- W4382201771 date "2022-12-16" @default.
- W4382201771 modified "2023-09-25" @default.
- W4382201771 title "Building term hierarchies using graph-based clustering" @default.
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- W4382201771 doi "https://doi.org/10.1145/3582768.3582807" @default.
- W4382201771 hasPublicationYear "2022" @default.
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