Name
Abstract | ||
|---|---|---|
Event classification at sentence level is an important Information Extraction task with applications in several NLP, IR, and personalization systems. Multi-label binary relevance (BR) are the state-of-art methods. In this work, we explored new multi-label methods known for capturing relations between event types. These new methods, such as the ensemble Chain of Classifiers, improve the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The low occurrence of multi-label sentences motivated the reduction of the hard imbalanced multi-label classification problem with low number of occurrences of multiple labels per instance to an more tractable imbalanced multiclass problem with better results (+ 4.6%). We report the results of adding new features, such as sentiment strength, rhetorical signals, domain-id (source-id and date), and key-phrases in both single-label and multi-label event classification scenarios. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Computer science,Rhetorical question,Information extraction,Artificial intelligence,Natural language processing,Sentence,Machine learning,Personalization,Binary number |
DocType | Volume | Citations |
Journal | abs/1403.6023 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luís Marujo | 1 | 224 | 14.86 |
| A. Gershman | 2 | 316 | 51.85 |
| Jaime G. Carbonell | 3 | 5019 | 724.15 |
| João Paulo Neto | 4 | 291 | 32.69 |
| David Martins de Matos | 5 | 152 | 29.19 |