Name
Abstract | ||
|---|---|---|
We present work on linking events and fluents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events, and we extend that work to include fluents as well, presenting a common methodology for linking both events and relations to timestamps within the same sentence. Our approach combines tree kernels with classical feature-based learning to exploit context and achieves competitive F1-scores on event-time linking, and comparable F1-scores for fluents. Our best systems achieve F1-scores of 0.76 on events and 0.72 on fluents. |
Year | Venue | Keywords |
|---|---|---|
2012 | EACL | temporal information,important enabler,certain period,tree kernel,previous research,temporal link,present work,best system,common methodology |
Field | DocType | Citations |
Enabling,Computer science,Timeline,Tree kernel,Exploit,Natural language processing,Timestamp,Artificial intelligence,Sentence | Conference | 11 |
PageRank | References | Authors |
0.60 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk Hovy | 1 | 490 | 40.44 |
| James Fan | 2 | 851 | 50.94 |
| Alfio Gliozzo | 3 | 257 | 24.97 |
| Siddharth Patwardhan | 4 | 1848 | 107.76 |
| Chris Welty | 5 | 117 | 15.49 |