Name
Abstract | ||
|---|---|---|
This paper presents a novel task of ordering given concepts (e.g., London, Paris, and Rome) on the basis of common attribute intensity expressed by a given adjective (e.g., safe) and proposes statistical ordering methods that integrate heterogeneous evidence extracted from text on concept ordering. This study is aimed at deriving collective wisdom on concept ordering from social media text. Solving this task is not only interesting from a sociological perspective but also beneficial in the practical sense for those who want to order unfamiliar entities in terms of subjective attributes that are hard to quantify in order to make correct decisions. Experiments on real-world concepts revealed a strong correlation between orderings obtained by our methods and gold-standard orderings. |
Year | Venue | Field |
|---|---|---|
2016 | IJCAI | Social media,Computer science,Sociological imagination,Collective wisdom,Artificial intelligence,Adjective,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
11 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatsuya Iwanari | 1 | 1 | 0.70 |
| Naoki Yoshinaga | 2 | 8 | 3.95 |
| Nobuhiro Kaji | 3 | 257 | 21.71 |
| Toshiharu Nishina | 4 | 1 | 0.36 |
| Masashi Toyoda | 5 | 388 | 49.87 |
| Masaru Kitsuregawa | 6 | 3188 | 831.46 |