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
fDate :
March 31 2009-April 2 2009
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;
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
DOI :
10.1109/CSIE.2009.651