tailieunhanh - Báo cáo khoa học: "Parsing Algorithms and Metrics"

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. . | Parsing Algorithms and Metrics Joshua Goodman Harvard University 33 Oxford St. Cambridge MA 02138 goodman@ Abstract 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. 1 Introduction In corpus-based approaches to parsing one is given a treebank a collection of text annotated with the correct parse tree and attempts to find algorithms that given unlabelled text from the treebank produce as similar a parse as possible to the one in the treebank. Various methods can be used for finding these parses. Some of the most common involve inducing Probabilistic Context-Free Grammars PCFGs and then parsing with an algorithm such as the Labelled Tree Viterbi Algorithm which maximizes the probability that the output of the parser the guessed tree is the one that the PCFG produced. This implicitly assumes that the induced PCFG does a good job modeling the corpus. There are many different ways to evaluate these parses. The most common include the Labelled Tree Rate also called the Viterbi Criterion or Exact Match Rate Consistent Brackets Recall Rate also called the Crossing Brackets Rate Consistent Brackets Tree Rate also called the Zero Crossing Brackets Rate and Precision and Recall. Despite the variety of evaluation metrics nearly all researchers .

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