Corrected co-training (Pierce & Cardie, 2001) and the closely related co-testing (Muslea et al., 2000) are active learning methods which exploit redundant views to reduce the cost of manually creating labeled training data. We extend these methods to statistical parsing algorithms for natural language. Because creating complex parse structures by hand is significantly more timeconsuming than selecting labels from a small set, it may be easier for the human to correct the learner’s partially accurate output rather than generate the complex label from scratch. The goal of our work is to minimize the number of corrections that the annotator must make. To reduce the human effort in correcting machine parsed sentences, we propose a novel approach, which we call one-sided corrected co-training and show that this method requires only a third as many manual annotation decisions as corrected co-training/co-testing to achieve the same improvement in performance. 1.

Because of the wide variety of contemporary practices used in the automatic syntactic parsing of natural languages, it has become necessary to analyze and evaluate the strengths and weaknesses of different approaches. This research is all the more necessary because there are currently no genre- and domain-independent parsers that are able to analyze unrestricted text with 100% preciseness (I use this term to refer to the correctness of analyses assigned by a parser). All these factors create a need for methods and resources that can be used to evaluate and compare parsing systems. This research describes: (1) A theoretical analysis of current achievements in parsing and parser evaluation. (2) A framework (called FEPa) that can be used to carry out practical parser evaluations and comparisons. (3) A set of new evaluation resources: FiEval is a Finnish treebank under construction, and MGTS and RobSet are parser evaluation resources in English. (4) The results of experiments in which the developed evaluation framework and the two resources for English were used for evaluating a set of selected parsers.