DocumentCode
495223
Title
Automatic Preposition Errors Correction Using Inductive Learning
Author
Ototake, Hokuto ; Araki, Kenji
Author_Institution
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
Volume
5
fYear
2009
fDate
March 31 2009-April 2 2009
Firstpage
335
Lastpage
338
Abstract
In this paper, we describe a system for correcting English preposition errors automatically. Non-native English writers often make these errors. Our system uses rules extracted automatically based on preposition context features, such as preceding and following nouns. Additional rules are generated recursively from the extracted rules using inductive learning. Our system achieves 82% accuracy and 32% coverage, which are competitive with other systems. Apart from the performance, it has an advantage of being more understandable while investigating why a given preposition was erroneous. This is because we use rules and they give this advantage over maximum entropy approaches.
Keywords
computer aided instruction; learning by example; linguistics; automatic preposition error correction; inductive learning; language learning; maximum entropy approach; nonnative English writer; preposition context feature; rule extraction; Computer errors; Computer science; Data mining; Dictionaries; Entropy; Error analysis; Error correction; Information science; Spatial databases; Writing; corpus; grammatical error correction; preposition error;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location
Los Angeles, CA
Print_ISBN
978-0-7695-3507-4
Type
conf
DOI
10.1109/CSIE.2009.651
Filename
5170553
Link To Document