Sequence Labeling with Reinforcement Learning and Ranking Algorithms

Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assign a label to each element in a sequence. Until now, this problem has mainly been addressed with Markov models and Dynamic Programming. We propose a new approach where the sequence labeling task is seen as a sequential decision process. This method is shown to be very fast with good gene ralization accuracy. Instead of searching for a globally optimal label sequence, we learn to construct this optimal sequence directly in a greedy fashion. First, we show that sequence labeling can be modelled using Markov Decision Processes, so that several Reinfo rcement Learning (RL) algorithms can be used for this task. Second, we introduce a new RL algorithm which is based on the ranking of local labeling decisions. Finally, we introduce an original method for sequence labeling, where labels are decided in an order-free manner.