Integer Linear Programming has recently been used for decoding in a number of probabilistic models in order to enforce global constraints. However, in certain applications, such as non-projective dependency parsing and machine translation, the complete formulation of the decoding problem as an integer linear program renders solving intractable. We present an approach which solves the problem incrementally, thus we avoid creating intractable integer linear programs. This approach is applied to Dutch dependency parsing and we show how the addition of linguistically motivated constraints can yield a significant improvement over stateof-the-art. 1

Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedy and only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging “equivalent ” stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster. 1

An open issue in data-driven dependency parsing is how to handle non-projective dependencies, which seem to be required by linguistically adequate representations, but which pose problems in parsing with respect to both accuracy and efficiency. Using data from five different languages, we evaluate an incremental deterministic parser that derives non-projective dependency structures in O(n 2) time, supported by SVM classifiers for predicting the next parser action. The experiments show that unrestricted non-projective parsing gives a significant improvement in accuracy, compared to a strictly projective baseline, with up to 35 % error reduction, leading to state-of-the-art results for the given data sets. Moreover, by restricting the class of permissible structures to limited degrees of non-projectivity, the parsing time can be reduced by up to 50 % without a significant decrease in accuracy. 1

We present a novel transition system for dependency parsing, which constructs arcs only between adjacent words but can parse arbitrary non-projective trees by swapping the order of words in the input. Adding the swapping operation changes the time complexity for deterministic parsing from linear to quadratic in the worst case, but empirical estimates based on treebank data show that the expected running time is in fact linear for the range of data attested in the corpora. Evaluation on data from five languages shows state-of-the-art accuracy, with especially good results for the labeled exact match score. 1

Finding a class of structures that is rich enough for adequate linguistic representation yet restricted enough for efficient computational processing is an important problem for dependency parsing. In this paper, we present a transition system for 2-planar dependency trees – trees that can be decomposed into at most two planar graphs – and show that it can be used to implement a classifier-based parser that runs in linear time and outperforms a stateof-the-art transition-based parser on four data sets from the CoNLL-X shared task. In addition, we present an efficient method for determining whether an arbitrary tree is 2-planar and show that 99 % or more of the trees in existing treebanks are 2-planar. 1

In this paper we describe an intuitionistic method for dependency parsing, where a classifier is used to determine whether a pair of words forms a dependency edge. And we also propose an effective strategy for dependency projection, where the dependency relationships of the word pairs in the source language are projected to the word pairs of the target language, leading to a set of classification instances rather than a complete tree. Experiments show that, the classifier trained on the projected classification instances significantly outperforms previous projected dependency parsers. More importantly, when this classifier is integrated into a maximum spanning tree (MST) dependency parser, obvious improvement is obtained over the MST baseline. 1

Graph-based dependency parsers suffer from the sheer number of higher order edges they need to (a) score and (b) consider during optimization. Here we show that when working with LP relaxations, large fractions of these edges can be pruned before they are fully scored—without any loss of optimality guarantees and, hence, accuracy. This is achieved by iteratively parsing with a subset of higherorder edges, adding higher-order edges that may improve the score of the current solution, and adding higher-order edges that are implied by the current best first order edges. This amounts to delayed column and row generation in the LP relaxation and is guaranteed to provide the optimal LP solution. For second order grandparent models, our method considers, or scores, no more than 6–13 % of the second order edges of the full model. This yields up to an eightfold parsing speedup, while providing the same empirical accuracy and certificates of optimality as working with the full LP relaxation. We also provide a tighter LP formulation for grandparent models that leads to a smaller integrality gap and higher speed. 1