Name
Abstract | ||
|---|---|---|
We present a novel beam-search decoder for grammatical error correction. The decoder iteratively generates new hypothesis corrections from current hypotheses and scores them based on features of grammatical correctness and fluency. These features include scores from discriminative classifiers for specific error categories, such as articles and prepositions. Unlike all previous approaches, our method is able to perform correction of whole sentences with multiple and interacting errors while still taking advantage of powerful existing classifier approaches. Our decoder achieves an F1 correction score significantly higher than all previous published scores on the Helping Our Own (HOO) shared task data set. |
Year | Venue | Keywords |
|---|---|---|
2012 | EMNLP-CoNLL | specific error category,interacting error,f1 correction score,decoder iteratively,previous published score,grammatical correctness,new hypothesis correction,grammatical error correction,previous approach,novel beam-search decoder |
Field | DocType | Volume |
Fluency,Computer science,Correctness,Beam search,Speech recognition,Error detection and correction,Natural language processing,Soft-decision decoder,Artificial intelligence,Classifier (linguistics),Discriminative model,Machine learning | Conference | D12-1 |
Citations | PageRank | References |
18 | 0.84 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Dahlmeier | 1 | 460 | 29.67 |
| Hwee Tou Ng | 2 | 4092 | 300.40 |