2026-09-20T15:05:22Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/78606 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

non-native text analysis Massung, Sean Alexander 2015-07-22T22:33:14Z 2015-07-22T22:33:14Z 2017-07-23T09:15:17Z 2015-05 2015-04-15 Non-native speakers of English far outnumber native speakers; English is the main language of books, newspapers, airports, air-traffic control, international business, academic conferences, science, technology, diplomacy, sports, international competitions, pop music, and advertising [1]. Online education in the form of MOOCs (massive online open courses) is also primarily in English— even teaching English. This creates enormous amounts of text written by non- native speakers, which in turn generates a need for grammar correction and analysis. Even aside from MOOCs, the number of English learners only in Asia alone is in the tens of millions. In response to this powerful motivation, we describe SYNTACTIC DIFF, a novel edit-based method for transforming sequences of words given a reference corpus. These transformations can be used directly or can be employed as features to represent text data in a wide variety of text mining scenarios. As case studies, we apply SYNTACTIC DIFF to four quite different tasks in non-native text analysis and show its benefit in each case. In the first task, we use weighted word edits with likelihood scoring for grammatical error correction. Our method is compared against systems in a grammar correction shared task, and we find that SYNTACTIC DIFF edits perform comparably while being much more general than the other methods. The second task is native language identification: a classification problem predicting the native language of a student writer based on English essays. We represent documents as vectors of edits, and show that a combination of unigram words and SYNTACTIC DIFF edits outperforms each representation individually. The third task is fluency scoring, in which we see if the manually categorized fluency levels of English students can be modeled by SYNTACTIC DIFF features. In the fourth task, we create clusters of student essays with similar errors via topic modeling, and find that the interpretability is significantly higher than an n-gram words approach. SYNTACTIC DIFF is highly customizable and able to capture syntactic differences from a reference corpus at the sentence, document, and subcorpus levels. This enables both a rich translation method and feature representation for many text mining tasks that deal with word usage and syntax beyond bag- of-words. In particular, this thesis focuses on non-native text analysis applications, though SYNTACTIC DIFF is not at all limited to that domain. Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01 The student, Sean Massung, accepted the attached license on 2015-04-10 at 17:40. The student, Sean Massung, submitted this Thesis for approval on 2015-04-10 at 17:49. This Thesis was approved for publication on 2015-04-15 at 17:01. DSpace SAF Submission Ingestion Package generated from Vireo submission #7821 on 2015-07-22 at 14:17:41 Made available in DSpace on 2015-07-22T22:33:14Z (GMT). No. of bitstreams: 2 MASSUNG-THESIS-2015.pdf: 273146 bytes, checksum: fc8087c7f787173e815db1bb72c56323 (MD5) LICENSE.txt: 4209 bytes, checksum: 92a2278025183b3a57e932d9889a00fb (MD5) Previous issue date: 2015-04-15 Embargo set by: Seth Robbins for item 79847 Lift date: 2017-07-22T22:34:16Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 79847 on 2017-07-23T09:15:17Z. application/pdf http://hdl.handle.net/2142/78606 en Copyright 2015 Sean Massung text mining natural language processing comparative text mining non-native text mining second language education non-native English speakers Non-native text analysis with Syntactic Diff, a general comparative text mining framework text text 2015-5 Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.