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Roth, Dan Ling, Shaoshi 2017-09-29T17:56:58Z 2017-09-29T17:56:58Z 2017-07-17 2017-08 In many natural language understanding applications, text processing requires comparing lexical units: words, phrases, name entities and sentences. A significant amount of research has taken place in studying evaluating similarity metrics between those units. In this thesis, we summarize some research work in computing lexical similarity. We describe a new approach to compute similarity between two spans of text, using multiple semantic-units level comparison measures to compute sentence-level similarity scores. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-09-29 without embargo terms The student, Shaoshi Ling, accepted the attached license on 2017-07-14 at 23:54. The student, Shaoshi Ling, submitted this Thesis for approval on 2017-07-14 at 23:59. This Thesis was approved for publication on 2017-07-17 at 10:53. DSpace SAF Submission Ingestion Package generated from Vireo submission #11483 on 2017-09-29 at 11:30:57 Made available in DSpace on 2017-09-29T17:56:58Z (GMT). No. of bitstreams: 2 LING-THESIS-2017.pdf: 480183 bytes, checksum: d2dbcaa9159c6fca4e6f7c9ba7d89035 (MD5) LICENSE.txt: 4209 bytes, checksum: ea74cab2d000156f512fae1ebfc17f72 (MD5) Previous issue date: 2017-07-17 application/pdf http://hdl.handle.net/2142/98404 en Copyright 2017 Shaoshi Ling Natural language processing (NLP) Similarity On lexical level matching text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.