Unregistered Biological Words Recognition by Q-Learning with Transfer Learning

Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, and F score results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning.