Name
Abstract | ||
|---|---|---|
Although researchers have shown increasing interest in extracting/classifying semantic relations, most previous studies have basically relied on lexical patterns between terms. This paper proposes a novel way to accomplish the task: a system that captures a physical size of an entity. Experimental results revealed that our proposed method is feasible and prevents the problems inherent in other methods. |
Year | Venue | Keywords |
|---|---|---|
2007 | SemEval@ACL | previous study,lexical pattern,svm-based semantic relation classification,physical size,semantic relation |
Field | DocType | Citations |
Computer science,Support vector machine,Artificial intelligence,Natural language processing,Semantic relation,Machine learning | Conference | 6 |
PageRank | References | Authors |
0.50 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eiji Aramaki | 1 | 371 | 45.89 |
| Takeshi Imai | 2 | 20 | 3.73 |
| Kengo Miyo | 3 | 24 | 5.65 |
| Kazuhiko Ohe | 4 | 115 | 15.91 |