  Natural Language Processing (NLP) models' current trend consists of using
increasingly more extra-data to build the best models as possible. It implies
more expensive computational costs and training time, difficulties for
deployment, and worries about these models' carbon footprint reveal a critical
problem in the future. Against this trend, our goal is to develop NLP models
requiring no extra-data and minimizing training time. To do so, in this paper,
we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov
Chain (PMC), for NLP segmentation tasks. We apply these models for three
classic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We
develop an original method to adapt these models for text segmentation's
specific challenges to obtain relevant performances with very short training
and execution times. PMC achieves equivalent results to those obtained by
Conditional Random Fields (CRF), one of the most applied models for these tasks
when no extra-data are used. Moreover, PMC has training times 30 times shorter
than the CRF ones, which validates this model given our objectives.
