Clustering Words by Projection Entropy
Computation and Language
2014-10-28 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1410.6830,
title = {Clustering Words by Projection Entropy},
author = {Işık Barış Fidaner and Ali Taylan Cemgil},
journal= {arXiv preprint arXiv:1410.6830},
year = {2014}
}
Comments
Accepted to NIPS 2014 Modern ML+NLP Workshop: http://www.cs.cmu.edu/~apparikh/nips2014ml-nlp/