Name
Abstract | ||
|---|---|---|
We examine the effectiveness of several unsupervised methods for latent semantics discovery as features for aspect-based sentiment analysis (ABSA). We use the shared task definition from SemEval 2014. In our experiments we use labeled and unlabeled corpora within the restaurants domain for two languages: Czech and English. We show that our models improve the ABSA performance and prove that our approach is worth exploring. Moreover, we achieve new state-of-the-art results for Czech. Another important contribution of our work is that we created two new Czech corpora within the restaurant domain for the ABSA task: one labeled for supervised training, and the other (considerably larger) unlabeled for unsupervised training. The corpora are available to the research community. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.13053/CyS-20-3-2469 | COMPUTACION Y SISTEMAS |
Keywords | Field | DocType |
Aspect-based sentiment analysis, latent semantics | Czech,SemEval,Computer science,Sentiment analysis,Natural language processing,Supervised training,Artificial intelligence,Machine learning,Semantics | Journal |
Volume | Issue | ISSN |
20 | 3 | 1405-5546 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomás Hercig | 1 | 5 | 3.12 |
| Tomas Brychcin | 2 | 96 | 9.92 |
| Lukás Svoboda | 3 | 2 | 1.07 |
| Michal Konkol | 4 | 67 | 6.42 |
| josef steinberger | 5 | 355 | 26.95 |