We explore some of the issues and challenges created by the incompatibility of diverse representation schemes for syntactic parsing. In particular, we examine the problem of output format conversion for evaluation of parsers that use different formalisms. We discuss recent related efforts, and present an evaluation of different parsers that use representations that vary not only in formalisms, but also in depth of syntactic information. We attempt to compare these parsers in a domain widely used for parser evaluation, the Wall Street Journal section of the Penn Treebank, and in the academic biomedical literature, where the use of parsing technologies is expected to contribute in practical applications, such as information extraction and text mining.

This paper presents our preliminary work on adaptation of parsing technology toward natural language query processing for biomedical domain. We built a small treebank of natural language queries, and tested a state-of-theart parser, the results of which revealed that a parser trained on Wall-Street-Journal articles and Medline abstracts did not work well on query sentences. We then experimented an adaptive learning technique, to seek the chance to improve the parsing performance on query sentences. Despite the small scale of the experiments, the results are encouraging, enlightening the direction for effective improvement. 1

We describe a new interactive annotation scheme between a human annotator who carries out simplified annotations on CFG trees, and a statistical parser that converts the human annotations automatically into a richly annotated HPSG treebank. In order to check the proposed scheme’s effectiveness, we performed automatic pseudo-annotations that emulate the system’s idealized behavior and measured the performance of the parser trained on those annotations. In addition, we implemented a prototype system and conducted manual annotation experiments on a small test set. 1

Supertagging is an important technique for deep syntactic analysis. A supertagger is usually trained independently of the parser using a sequence labeling method. This presents an inconsistent training objective between the supertagger and the parser. In this paper, we propose a forest-guided supertagger training method to alleviate this problem by incorporating global grammar constraints into the supertagging process using a CFGfilter. It also provides an approach to make the supertagger and the parser more tightly integrated. The experiment shows that using the forest-guided trained supertagger, the parser got an absolute 0.68% improvement from baseline in F-score for predicate-argument relation recognition accuracy and achieved a competitive result of 89.31 % with a faster parsing speed, compared to a state-of-the-art HPSG parser. 1