An Adaptive Method for Organization Name Disambiguation with Feature Reinforcing

Twitter is an online social networking, which has become an important source of information for marketing strategies and online reputation management. In this paper, we probe the problem of organization name disambiguation on twitter messages. This task is challenging due to the fact of lacking sufficient information both from organization and the tweets. We mine organization information from web sources to train a general classifier. Further, we mine tweets information. We train an adaptive classifier for a given organization name with more features derived from twitter messages labeled by the general classifier. The experiments on WePS-3 show mining web sources to enrich organization are effective. The adaptive classifier trained for a given organization is promising.