Name
Abstract | ||
|---|---|---|
Finite-state chunking and tagging methods are very fast for annotating non-hierarchical syntactic information, and are often applied in applications that do not require full syntactic analyses. Scenarios such as incremental machine translation may benefit from some degree of hierarchical syntactic analysis without requiring fully connected parses. We introduce hedge parsing as an approach to recovering constituents of length up to some maximum span L. This approach improves efficiency by bounding constituent size, and allows for efficient segmentation strategies prior to parsing. Unlike shallow parsing methods, hedge parsing yields internal hierarchical structure of phrases within its span bound. We present the approach and some initial experiments on different inference strategies. |
Year | Venue | DocType |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | Conference |
Volume | Citations | PageRank |
P14-2 | 1 | 0.36 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahsa Yarmohammadi | 1 | 7 | 1.16 |
| Aaron Dunlop | 2 | 29 | 3.10 |
| Brian Roark | 3 | 20 | 4.62 |