Sequence Labelling with Reinforcement Learning and Ranking Algorithms

Abstract : 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 generalization 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 Reinforcement 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.
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Submitted on : Wednesday, June 22, 2016 - 4:51:50 PM
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Francis Maes, Ludovic Denoyer, Patrick Gallinari. Sequence Labelling with Reinforcement Learning and Ranking Algorithms. 18th European Conference on Machine Learning, ECML 2007, Sep 2007, Warsaw, Poland. pp.648-657, ⟨10.1007/978-3-540-74958-5_64⟩. ⟨hal-01336187⟩



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