Name
Abstract | ||
|---|---|---|
In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners' collocation usages in their academic writing with satisfactory results. |
Year | Venue | Keywords |
|---|---|---|
2010 | ACL (Short Papers) | native speaker,data-driven classifier,important discourse function,non-native speaker,online collocation,collocation usage,essential characteristic,automatic collocation suggestion,academic community,academic writing,collocation suggestion,machine learning |
Field | DocType | Volume |
Computer science,Academic writing,Natural language processing,Artificial intelligence,Classifier (linguistics),Academic community,Linguistics,Collocation | Conference | P10-2 |
Citations | PageRank | References |
8 | 0.65 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Cheng Wu | 1 | 70 | 13.30 |
| Yu-Chia Chang | 2 | 96 | 8.01 |
| Teruko Mitamura | 3 | 719 | 86.39 |
| Jason S. Chang | 4 | 345 | 62.64 |