Shallow Parsing using Specialized HMMs
Antonio Molina, Ferran Pla;
2(Mar):595-613, 2002.
Abstract
We present a unified technique to solve different 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
different specialization criteria. The results obtained are in line with the
results reported for most of the relevant state-of-the-art
approaches.
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