Paper: Collective Tweet Wikification based on Semi-supervised Graph Regularization

ACL ID P14-1036
Title Collective Tweet Wikification based on Semi-supervised Graph Regularization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Wikification for tweets aims to automat- ically 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 ev- idence from multiple mentions and con- cepts is more appropriate than a non- collecitve approach which links each men- tion at a time. In addition, it is chal- lenging 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 multi- ple tweets through three fine-grained re- lations. In order to identify semantically- related mentions for collective inference, we detect meta path-...