Name
Abstract | ||
|---|---|---|
We introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task. |
Year | Venue | DocType |
|---|---|---|
2017 | EACL | Conference |
Volume | Citations | PageRank |
abs/1610.00479 | 3 | 0.40 |
References | Authors | |
67 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hinrich Schuetze | 1 | 33 | 2.73 |
| Heike Adel | 2 | 62 | 15.03 |
| Ehsaneddin Asgari | 3 | 26 | 4.88 |