Supervised and Semi-supervised Methods based Organization Name Disambiguity

Twitter is a widespread social media, which rapidly gained worldwide popularity. Pursuing on the problem of finding related tweets to a given organization, we propose supervised and semi-supervised based methods. This is a challenging task due to the potential organization name ambiguity. The tweets and organization contain little information. The organizations in training data are different with those in test data, which leads that we could not train a classifier to a certain organization. Therefore, we induce external resources to enrich the information of organization. Supervised and semisupervised methods are adopted in two stages to classify the tweets. This is a try to utilize both training and test data for this specific task. Our experimental results on WePS-3 are primary and encouraging, they prove the proposed techniques are effective in performing the task.