Name
Abstract | ||
|---|---|---|
While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models. However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to reinvent the wheel. One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for part-of-speech and semantic tagging for Welsh using a pre-trained embedding model from Fast-Text. Our model's performance was compared with those of the existing rule-based stand-alone taggers for part-of-speech and semantic taggers. Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/w19-4332 | 4TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2019) |
Field | DocType | Citations |
Embedding,Computer science,Natural language processing,Artificial intelligence,Word embedding,Artificial neural network,Machine learning,Welsh | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignatius Ezeani | 1 | 1 | 2.73 |
| Scott S. L. Piao | 2 | 93 | 12.65 |
| Steven Neale | 3 | 0 | 0.34 |
| Paul Rayson | 4 | 538 | 54.59 |
| Dawn Knight | 5 | 5 | 2.35 |