We present a novel semi-supervised training algorithm for learning dependency parsers.

By combining a supervised large margin loss with an unsupervised least squares loss, a dis-criminative, convex, semi-supervised learning algorithm can be obtained that is applicable to large-scale problems.

heavy dependence on annotated corpora—many researchers have investigated semi-supervised learning techniques that can take both labeled and unlabeled training data as input.

Unfortunately, although significant recent progress has been made in the area of semi-supervised learning, the performance of semi-supervised learning algorithms still fall far short of expectations, particularly in challenging real-world tasks such as natural language parsing or machine translation.

A large number of distinct approaches to semi-supervised training algorithms have been investigated in the literature (Bennett and Demiriz, 1998; Zhu et al., 2003; Altun et al., 2005; Mann and McCallum, 2007).

We present a simple and effective semi-supervised method for training dependency parsers.

In this paper, we have presented a simple but effective semi-supervised learning approach and demonstrated that it achieves substantial improvement over a competitive baseline in two broad-coverage depen-

In this paper, we introduce lexical intermediaries via a simple two-stage semi-supervised approach.

In general, semi-supervised learning can be motivated by two concerns: first, given a fixed amount of supervised data, we might wish to leverage additional unlabeled data to facilitate the utilization of the supervised corpus, increasing the performance of the model in absolute terms.

We show that our semi-supervised approach yields improvements for fixed datasets by performing parsing experiments on the Penn Treebank (Marcus et al., 1993) and Prague Dependency Treebank (Hajic, 1998; Hajic et al., 2001) (see Sections 4.1 and 4.3).

Semi-supervised phrase structure parsing has been previously explored by McClosky et al.

The results for the semi-supervised models are nonconclusive.

We experiment with three model setups: Supervised, semi-supervised , and unsupervised.

In Model II, the semi-supervised setup, the training data is used to initialize the Expectation-Maximization (EM) algorithm (Dempster et al., 1977) and the unlabeled data, described in Section 3.1, updates the initial estimates.

The unsupervised setup, Model III, is similar to the semi-supervised setup except that the EM algorithm is initialized using an informed guess by the authors.