Name
Abstract | ||
|---|---|---|
In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present results showing that models built using these human-comprehensible features are competitive with models trained with Bag of Words features. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Bag-of-words model,Computer science,Speech recognition,Machine-readable dictionary,Artificial intelligence,Natural language processing |
DocType | Volume | Citations |
Journal | abs/1606.07545 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camille Jandot | 1 | 0 | 0.34 |
| Patrice Y. Simard | 2 | 1112 | 155.00 |
| Max Chickering | 3 | 57 | 2.78 |
| David Grangier | 4 | 816 | 41.60 |
| Jina Suh | 5 | 178 | 10.04 |