  Many different metrics exist for evaluating parsing results, including
Viterbi, Crossing Brackets Rate, Zero Crossing Brackets Rate, and several
others. However, most parsing algorithms, including the Viterbi algorithm,
attempt to optimize the same metric, namely the probability of getting the
correct labelled tree. By choosing a parsing algorithm appropriate for the
evaluation metric, better performance can be achieved. We present two new
algorithms: the ``Labelled Recall Algorithm,'' which maximizes the expected
Labelled Recall Rate, and the ``Bracketed Recall Algorithm,'' which maximizes
the Bracketed Recall Rate. Experimental results are given, showing that the two
new algorithms have improved performance over the Viterbi algorithm on many
criteria, especially the ones that they optimize.
