Abstract

We present a unified technique to solve di#erent shallow parsing tasks as a tagging problem using a Hidden Markov Model-based approach (HMM). This technique consists of the incorporation of the relevant information for each task into the models. To do this, the training corpus is transformed to take into account this information. In this way, no change is necessary for either the training or tagging process, so it allows for the use of a standard HMM approach. Taking into account this information, we construct a Specialized HMM which gives more complete contextual models. We have tested our system on chunking and clause identification tasks using di#erent specialization criteria. The results obtained are in line with the results reported for most of the relevant state-of-the-art approaches.

Citations