Name
Title | ||
|---|---|---|
Reinvestigating the Classification Approach to the Article and Preposition Error Correction. |
Abstract | ||
|---|---|---|
In this work, we reinvestigate the classifier-based approach to article and preposition error correction going beyond linguistically motivated factors. We show that state-of-the-art results can be achieved without relying on a plethora of heuristic rules, complex feature engineering and advanced NLP tools. A proposed method for detecting spaces for article insertion is even more efficient than methods that use a parser. We examine automatically trained word classes acquired by unsupervised learning as a substitution for commonly used part-of-speech tags. Our best models significantly outperform the top systems from CoNLL-2014 Shared Task in terms of article and preposition error correction. |
Year | Venue | Field |
|---|---|---|
2015 | LTC | Heuristic,Computer science,Error detection and correction,Feature engineering,Unsupervised learning,Artificial intelligence,Natural language processing,Parsing,Classifier (linguistics) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roman Grundkiewicz | 1 | 109 | 11.75 |
| Marcin Junczys-Dowmunt | 2 | 312 | 24.24 |