[Molina and Pla(2002)] presented a shallow parser based on Hidden Markov Models (HMMs). HMMS are routinely used in speech recognition and part-of-speech tagging (POS tagging). Here, the HMM was used to find the most probable sequence of output shallow parsing labels for the current sequence of inputs. Unlike with the previous MBL approach to shallow parsing (which is classification-based), this approach used a generation approach. Their generative model enabled information about the whole sentence to be taken into account when determining the output shallow parsing label for each word, since it is the probability of the whole sequence of output tags occurring given the current input that is maximised (and not just the probability of individual decisions). The authors' HMMs are applied to a variety of shallow parsing tasks.
Various ways of encoding the task were shown to produce different results. Clearly, this suggests that feature specification is an important issue. Interestingly enough, the authors, whilst not using ensemble learning methods, produced results comparable with systems which did use such techniques. Here, the obvious comparison is with the MBL paper mentioned in section 2.1. An interesting possibility here is that their generative model (which allows previous decisions to directly influence future decisions) emulates the ability of ensemble learners to correct for classifiers which do not take previous decisions into account.