Name
Abstract | ||
|---|---|---|
Automatic negation scope detection is a task that has been tackled using different classifiers and heuristics. Most systems are however 1) highly-engineered, 2) English-specific, and 3) only tested on the same genre they were trained on. We start by addressing 1) and 2) using a neural network architecture. Results obtained on data from the *SEM2012 shared task on negation scope detection show that even a simple feed-forward neural network using word-embedding features alone, performs on par with earlier classifiers, with a bi-directional LSTM outperforming all of them. We then address 3) by means of a specially-designed synthetic test set; in doing so, we explore the problem of detecting the negation scope more in depth and show that performance suffers from genre effects and differs with the type of negation considered. |
Year | Venue | Field |
|---|---|---|
2016 | PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | Negation,Computer science,Artificial intelligence,Natural language processing,Artificial neural network,Machine learning |
DocType | Volume | Citations |
Conference | P16-1 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Federico Fancellu | 1 | 7 | 4.18 |
| Adam Lopez | 2 | 538 | 34.69 |
| Bonnie Lynn Webber | 3 | 1511 | 317.14 |