Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many different metrics exist for evaluating parsing results, including Viterbi, Crossing Brackets Rate, Zero Crossing Brackets Rate, and several others. Hover, 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.  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.
['Joshua T. Goodman']
I am writing a research paper on a idea Parsing Algorithms and Metrics
[{"from": "human", "value": "I am writing a research paper on a idea Parsing Algorithms and Metrics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many different metrics exist for evaluating parsing results, including Viterbi, Crossing Brackets Rate, Zero Crossing Brackets Rate, and several others. Hover, 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.  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."}]