Name
Abstract | ||
|---|---|---|
We introduce a new method for learning to detect grammatical errors in learner's writing and provide suggestions. The method involves parsing a reference corpus and inferring grammar patterns in the form of a sequence of content words, function words, and parts-of-speech (e.g., "play ~ role in Ving" and "look forward to Ving"). At runtime, the given passage submitted by the learner is matched using an extended Levenshtein algorithm against the set of pattern rules in order to detect errors and provide suggestions. We present a prototype implementation of the proposed method, EdIt, that can handle a broad range of errors. Promising results are illustrated with three common types of errors in non-native writing. |
Year | Venue | Keywords |
|---|---|---|
2011 | ACL (System Demonstrations) | pattern grammar,function word,extended levenshtein algorithm,common type,inferring grammar pattern,broad-coverage grammar checker,content word,non-native writing,broad range,new method,grammatical error |
Field | DocType | Volume |
Programming language,Computer science,Grammar,Artificial intelligence,Natural language processing,Pattern grammar,Parsing,Machine learning | Conference | P11-4 |
Citations | PageRank | References |
2 | 0.38 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chung-Chi Huang | 1 | 28 | 9.43 |
| Mei-Hua Chen | 2 | 12 | 5.69 |
| Shih-Ting Huang | 3 | 70 | 8.56 |
| Jason S. Chang | 4 | 345 | 62.64 |