Name
Abstract | ||
|---|---|---|
Wikification for tweets aims to automatically identify each concept mention in a tweet and link it to a concept referent in a knowledge base (e.g., Wikipedia). Due to the shortness of a tweet, a collective inference model incorporating global evidence from multiple mentions and concepts is more appropriate than a non-collecitve approach which links each mention at a time. In addition, it is challenging to generate sufficient high quality labeled data for supervised models with low cost. To tackle these challenges, we propose a novel semi-supervised graph regularization model to incorporate both local and global evidence from multiple tweets through three fine-grained relations. In order to identify semantically-related mentions for collective inference, we detect meta path-based semantic relations through social networks. Compared to the state-of-the-art supervised model trained from 100% labeled data, our proposed approach achieves comparable performance with 31% labeled data and obtains 5% absolute F1 gain with 50% labeled data. |
Year | Venue | Field |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | Social network,Inference,Computer science,Referent,Graph regularization,Natural language processing,Artificial intelligence,Labeled data,Knowledge base,Machine learning |
DocType | Volume | Citations |
Conference | P14-1 | 19 |
PageRank | References | Authors |
0.67 | 40 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongzhao Huang | 1 | 126 | 6.64 |
| Yunbo Cao | 2 | 1082 | 63.12 |
| Xiaojiang Huang | 3 | 19 | 0.67 |
| Heng Ji | 4 | 1544 | 127.27 |
| Chin-Yew Lin | 5 | 3170 | 242.72 |