Name
Abstract | ||
|---|---|---|
This paper describes IDEST, a new method for learning paraphrases of event patterns. It is based on a new neural network architecture that only relies on the weak supervision signal that comes from the news published on the same day and mention the same real-world entities. It can generalize across extractions from different dates to produce a robust paraphrase model for event patterns that can also capture meaningful representations for rare patterns. We compare it with two state-of-the-art systems and show that it can attain comparable quality when trained on a small dataset. Its generalization capabilities also allow it to leverage much more data, leading to substantial quality improvements. |
Year | Venue | Field |
|---|---|---|
2015 | HLT-NAACL | Leverage (finance),Computer science,Neural network architecture,Paraphrase,Natural language processing,Artificial intelligence,Distributed representation,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.36 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Krause | 1 | 26 | 5.04 |
| Enrique Alfonseca | 2 | 1033 | 66.61 |
| Katja Filippova | 3 | 361 | 19.59 |
| daniele pighin | 4 | 289 | 18.72 |