Name
Title | ||
|---|---|---|
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages. |
Abstract | ||
|---|---|---|
In this work, we present new state-of-the-art results of 93.59,% and 79.59,% for Turkish and Czech named entity recognition based on the model of (Lample et al., 2016). We contribute by proposing several schemes for representing the morphological analysis of a word in the context of named entity recognition. We show that a concatenation of this representation with the word and character embeddings improves the performance. The effect of these representation schemes on the tagging performance is also investigated. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Turkish,Czech,Computer science,Artificial intelligence,Natural language processing,Concatenation,Named-entity recognition |
DocType | Volume | Citations |
Journal | abs/1706.00506 | 0 |
PageRank | References | Authors |
0.34 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gungor, Onur | 1 | 0 | 0.68 |
| Eray Yildiz | 2 | 0 | 0.34 |
| Suzan Uskudarli | 3 | 10 | 5.19 |
| Tunga Güngör | 4 | 342 | 32.67 |