Automatic transfer learning for short text mining.

As a new emerging technique, transfer learning enjoys the advantage of integrating the well-learnt knowledge from another related work to facilitate an improved learning result of one task. Most of the existing transfer learning methods are designed for long texts and short texts. However, the latter one distinguishes from the former one in terms of its sparse nature, noise words, syntactical structure, and colloquial terminologies used. A transfer learning algorithm called automatic transfer learning (AutoTL) is proposed for short text mining. By transferring knowledge automatically learnt from the online information, the proposed method enables training data to be selected automatically. Furthermore, it does not make any a priori assumption about probability distribution. Our experimental results on 20Newsgroups, Simulated Real Auto Aviation, and Reuter-21578 validate the higher performance of the proposed AutoTL over several state-of-of-the-art methods. [ABSTRACT FROM AUTHOR]