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Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard “Charniak parser” checks in at a labeled precisionrecall f -measure of 89.7% on the Penn WSJ test set, but only 82.9% on the test set. | Reranking and Self-Training for Parser Adaptation David McClosky Eugene Charniak and Mark Johnson Brown Laboratory for Linguistic Information Processing BLLIP Brown University Providence RI 02912 dmcc ec mj @cs.brown.edu Abstract Statistical parsers trained and tested on the Penn Wall Street Journal wsj treebank have shown vast improvements over the last 10 years. Much of this improvement however is based upon an ever-increasing number of features to be trained on typically the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard Charniak parser checks in at a labeled precisionrecall f-measure of 89.7 on the Penn WSJ test set but only 82.9 on the test set from the Brown treebank corpus. This paper should allay these fears. In particular we show that the reranking parser described in Charniak and Johnson 2005 improves performance of the parser on Brown to 85.2 . Furthermore use of the self-training techniques described in Mc-Closky et al. 2006 raise this to 87.8 an error reduction of 28 again without any use of labeled Brown data. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.4 . 1 Introduction Modern statistical parsers require treebanks to train their parameters but their performance declines when one parses genres more distant from the training data s domain. Furthermore the treebanks required to train said parsers are expensive and difficult to produce. Naturally one of the goals of statistical parsing is to produce a broad-coverage parser which is relatively insensitive to textual domain. But the lack of corpora has led to a situation where much of the current work on parsing is performed on a single domain using training data from that domain the Wall Street Journal wsj section of the Penn Treebank Marcus et al. 1993 . Given the aforementioned costs it is unlikely that many .
