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Weights
The weights that are associated with the edges of the graph can be
sensitive to the following factors.
 Acoustic score. Obviously, the acoustic score
present in the wordgraph is an important factor. The
acoustic scores are derived from probabilities by taking the negative
logarithm. For this reason we aim to minimize this score. If edges
are combined, then we have to sum the corresponding acoustic scores.
 Number of `skips'. We want to minimize the number of skips,
in order to obtain a preference for the maximal projections found by
the parser. Each time we select a skip edge, the number of skips is
increased by 1.
 Number of maximal projections. We want to minimize the number of
such maximal projections, in order to obtain a preference for more
extended linguistic analyses over a series of smaller ones. Each
time we select a category edge, this number is
increased by 1.
 Quality of the QLF in relation to the context. We are
experimenting with evaluating the quality of a given QLF in
relation to the dialogue context, in particular the question
previously asked by the system [26].
 Ngram statistics. We have experimented with bigrams and
trigrams. Ngram scores are expressed as negative logarithms of
probabilities. This implies that combining Ngram scores requires
addition, and that lower scores imply higher probability.
The only requirement we make to ensure that efficient graph searching
algorithms are applicable is that weights are uniform. This
means that a weight for an edge leaving a vertex v_{i} is independent
of how state v_{i} was reached.
In order to be able to compute with such multidimensional weights, we
express weights as tuples
. For each
cost component c_{i} we specify an initial weight, and we need to specify for
each edge the weight of each cost component. To specify how weights are
updated if a path is extended, we use the function
that maps a
pair of a multidimensional weight and an edge a to multidimensional
weight. ^{7} Moreover, we need to define an ordering on such
tuples. In order to experiment with different implementations of this
idea we refer to such a collection of specifications as a
method. Summarizing, such a weight method is a triple
where

is the initial weight;

is the update weight function;
 is an ordering on weights
Subsections
Next: Example: speech method.
Up: Robust parsing of wordgraphs
Previous: Annotated wordgraph
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