Name
Abstract | ||
|---|---|---|
Finding the right representations for words is critical for building accurate NLP systems when domain-specific labeled data for the task is scarce. This article investigates novel techniques for extracting features from n-gram models, Hidden Markov Models, and other statistical language models, including a novel Partial Lattice Markov Random Field model. Experiments on part-of-speech tagging and information extraction, among other tasks, indicate that features taken from statistical language models, in combination with more traditional features, outperform traditional representations alone, and that graphical model representations outperform n-gram models, especially on sparse and polysemous words. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1162/COLI_a_00167 | Computational Linguistics |
Field | DocType | Volume |
Markov random field,Computer science,Information extraction,Artificial intelligence,Natural language processing,Graphical model,Labeled data,Hidden Markov model,Language model,Machine learning | Journal | 40 |
Issue | ISSN | Citations |
1 | 0891-2017 | 23 |
PageRank | References | Authors |
0.95 | 75 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Huang | 1 | 506 | 56.44 |
| Arun Ahuja | 2 | 72 | 7.45 |
| Doug Downey | 3 | 1908 | 119.79 |
| Yi Yang | 4 | 27 | 1.68 |
| Yuhong Guo | 5 | 774 | 49.28 |
| Alexander Yates | 6 | 898 | 51.53 |