While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems. In this paper, we propose an approach to joint POS tagging and dependency parsing using transitionbased neural networks. Three neural network based classifiers are desi...

We present experiments with a dependency parsing model defined on rich factors. Our model represents dependency trees with factors that include three types of relations between the tokens of a dependency and their children. We extend the projective parsing algorithm of Eisner (1996) for our case, and train models using the averaged perceptron. Our experiments show that considering higher-order ...

Transforming syntactic representations in order to improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based representations. In this paper, we show that similar transformations can give substantial improvements also in data-driven dependency parsing. Experiments on the Prague Dependency Treebank show that systematic transformations of coor...

In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase ...

Dependency parsing has been a prime focus of Natural Language Processing. The present paper was attempting to elucidate the relationship between prosodic variation and syntactic structure within the framework of dependency parsing. The study adopted Harbin Institute of Technology (HIT) dependency parsing tool to annotate Chinese spoken discourse corpus. Duration variation and stress distributio...

This paper proposes to learn languageindependent word representations to address cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source language. We first combine all sentences from both languages to induce real-valued distributed representation of words under ...

Even with high overall parsing accuracy, datadriven parsers often make errors in the assignment of core grammatical functions such as subject and object. Starting from a detailed error analysis of a state-of-the-art dependency parser for Swedish, we show that the addition of linguistically motivated features targeting specific error types may lead to substantial improvements, both for specific ...

We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms. Our approach, called ‘iterated reranking’ (IR), starts with dependency trees generated by an unsupervised parser, and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees. Ou...

In this paper we extend the maximum spanning tree (MST) dependency parsing framework of McDonald et al. (2005c) to incorporate higher-order feature representations and allow dependency structures with multiple parents per word. We show that those extensions can make the MST framework computationally intractable, but that the intractability can be circumvented with new approximate parsing algori...