Name
Abstract | ||
|---|---|---|
In the task of event coreference resolution, recent work has shown the need to perform not only full coreference but also partial coreference of events. We show that subevents can form a particular hierarchical event structure. This paper examines a novel two-stage approach to finding and improving subevent structures. First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference. Second, we propose a method to improve subevent structure based on subevent clusters detected by the model. Using a corpus in the Intelligence Community domain, we show that the method achieves over 3.2 BLANC F1 gain in detecting subevent relations against the logistic regression model. |
Year | Venue | Keywords |
|---|---|---|
2014 | LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | event coreference resolution,subevent structure,event relation learning |
Field | DocType | Citations |
Coreference,Computer science,Speech recognition,Artificial intelligence,Natural language processing | Conference | 7 |
PageRank | References | Authors |
0.48 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Araki | 1 | 48 | 8.31 |
| Zhengzhong Liu | 2 | 37 | 7.69 |
| Eduard H. Hovy | 3 | 7450 | 663.27 |
| Teruko Mitamura | 4 | 719 | 86.39 |