Improved text classification through label clustering Public

http://ir.library.oregonstate.edu/concern/honors_college_theses/zw12z723q

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  • This paper introduces an approach to text classification for semi-structured label systems that have poor performance with standard methods. With the perspective that perfect classification for such a system is unattainable, we demonstrate an automated procedure to isolate the learnable elements of the problem. Through analysis of an example dataset, we identify attributes of the label system that hinder performance and demonstrate through manual methods that minimizing these attributes will lead to improved performance. Further we present that label clustering effectively minimizes these attributes. We then show that with a combination of frequency, co-occurrence, and document similarity we are able to construct label clusters in an automated fashion. Finally, we demonstrate that by using label clusters we are able to improve classification performance without excessively limiting the label space.
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  • description.provenance : Submitted by Christopher Vanderschuere (vandersc@onid.orst.edu) on 2015-06-09T20:44:44ZNo. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)final-draft.pdf: 3608203 bytes, checksum: 8c4f3403cf559b4b0d1225547569a2c7 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2015-06-10T13:31:08Z (GMT) No. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)final-draft.pdf: 3608203 bytes, checksum: 8c4f3403cf559b4b0d1225547569a2c7 (MD5)
  • description.provenance : Made available in DSpace on 2015-06-10T13:31:08Z (GMT). No. of bitstreams: 2license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5)final-draft.pdf: 3608203 bytes, checksum: 8c4f3403cf559b4b0d1225547569a2c7 (MD5)

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