Clustering Words by Projection Entropy
This work addresses text analysis for literary researchers, but it is incremental as it applies an existing algorithm to a new domain.
The authors tackled the problem of clustering words in a literary text by applying entropy agglomeration (EA) to minimize projection entropy, and found that EA is useful in capturing significant relationships among words despite its simplicity.
We apply entropy agglomeration (EA), a recently introduced algorithm, to cluster the words of a literary text. EA is a greedy agglomerative procedure that minimizes projection entropy (PE), a function that can quantify the segmentedness of an element set. To apply it, the text is reduced to a feature allocation, a combinatorial object to represent the word occurences in the text's paragraphs. The experiment results demonstrate that EA, despite its reduction and simplicity, is useful in capturing significant relationships among the words in the text. This procedure was implemented in Python and published as a free software: REBUS.