| we present a new parser for parsing down to penn tree-bank style parse trees [16] that achieves 90.1% average precision/recall for sentences of length < 40, and 89.5% for sentences of length < 100, when trained and tested on the previously established [5,9,10,15,17] "standard" sections of the wall street journal tree-bank. this represents a 13% decrease in error rate over the best single-parser results on this corpus [9]. following [5,10], our parser is based upon a probabilistic generative model. that is, for all sentences s and all parses 7r, the parser assigns a probability p(s , 7r) = p(r), the equality holding when we restrict consideration to 7r whose yield * this research was supported in part by nsf grant lis sbr 9720368. the author would like to thank mark johnson and all the rest of the brown laboratory for linguistic information processing. is s. then for any s the parser returns the parse ir that maximizes this probability. that is, the parser implements the function arg maxrp(7r s) = arg maxirp(7r, s) = arg maxrp(w). what fundamentally distinguishes probabilistic generative parsers is how they compute p(r), and it is to that topic we turn next.it is to this project that our future parsing work will be devoted. what fundamentally distinguishes probabilistic generative parsers is how they compute p(r), and it is to that topic we turn next. we present a new parser for parsing down to penn tree-bank style parse trees [16] that achieves 90.1% average precision/recall for sentences of length < 40, and 89.5% for sentences of length < 100, when trained and tested on the previously established [5,9,10,15,17] "standard" sections of the wall street journal tree-bank. indeed, we initiated this line of work in an attempt to create a parser that would be flexible enough to allow modifications for parsing down to more semantic levels of detail. we have presented a lexicalized markov grammar parsing model that achieves (using the now standard training/testing/development sections of the penn treebank) an average precision/recall of 91.1% on sentences of length < 40 and 89.5% on sentences of length < 100. this corresponds to an error reduction of 13% over the best previously published single parser results on this test set, those of collins [9]. in the previous sections we have concentrated on the relation of the parser to a maximumentropy approach, the aspect of the parser that is most novel. |