2026-09-19T10:00:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/46741 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

document classification email routing A study of automatic email routing for an information technology help desk text Zhai, ChengXiang Di Febo, Joseph 2014-01-16T18:00:52Z 2014-01-16T18:00:52Z 2013-12 2014-01-16T18:00:52Z 2013-12 Document classification has been a classic problem in both machine learning and information retrieval. One domain for document classification is automatic email routing. Given an email (a document), the system attempts to guess the location that the email should be routed to. An automatic system would in theory be able to replace a person doing the job of sorting emails, which can save time and money. However, incorrectly sorted emails would then need to be re-sorted manually, so it is important for the system to be accurate. The Engineering IT department at the University of Illinois at Urbana-Champaign has a helpdesk that users can email with technical problems. The IT department services the entire College of Engineering, encompassing many departments, so they need a way to sort emails based on which IT professional would be best suited to fixing a user’s problem. Thus, this problem would be well suited for some type of document classification solution. However, the helpdesk stands out from similar problems; the IT department already employs an email routing technique that is already quite accurate. It becomes very obvious that a stand-alone document classification solution would be subpar, but perhaps combining it with the existing routing method would provide higher accuracy. In this thesis, we explore ways to combine the classic document classification techniques with the existing routing strategy used by the helpdesk. We test out different text-based features, but we find that since the existing method is already very accurate, it is quite difficult to improve on. Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2013-12-11T17:58:20Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 difebo_joseph.docx: 39263 bytes, checksum: 051b28ea3e4dc93d559ae8b55e55831e (MD5) difebo_joseph.pdf: 333453 bytes, checksum: b74f9f15c7c2d213e5645613d1f46476 (MD5) Made available in DSpace on 2014-01-16T18:00:52Z (GMT). No. of bitstreams: 3 Joseph_Di Febo.pdf: 333536 bytes, checksum: 9e88cd600da79417b9627debb3359b4b (MD5) difebo_joseph.docx: 39292 bytes, checksum: 8f28f508ec535ff369dfb7fa62aa2dab (MD5) license.txt: 4063 bytes, checksum: 9bb72955e81e37536fa899493bda911d (MD5) http://hdl.handle.net/2142/46741 en Copyright 2013 Joseph Di Febo information retrieval Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:CS:BS/MS Program - UIUC 10KS1854MS