It has been observed that the inclusion of morphosyntactic information in dependency treebanks is crucial to obtain high results in dependency parsing for some languages. In this paper we explore in depth to what extent it is useful to include morphological features, and the impact of diverse morphosyntactic annotations on statistical dependency parsing of Spanish. For this, we give a detailed ...

Arc contractions in syntactic dependency graphs can be used to decide which graphs are trees. The paper observes that these contractions can be expressed with weighted finite-state transducers (weighted FST) that operate on stringencoded trees. The observation gives rise to a finite-state parsing algorithm that computes the parse forest and extracts the best parses from it. The algorithm is cus...

Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In this paper, we develop an unsupervised dependency parsing model based on the CRF autoencoder. The encoder part of our model is discriminative and globally normalized which allo...

We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also ...

Modern statistical dependency parsers assign lexical heads to punctuations as well as words. Punctuation parsing errors lead to low parsing accuracy on words. In this work, we propose an alternative approach to addressing punctuation in dependency parsing. Rather than assigning lexical heads to punctuations, we treat punctuations as properties of their neighbouring words, used as features to gu...

We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms ...

Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser fo...

This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between “portions” of words – called inflectional groups. We have explored statistical models tha...

Usually unsupervised dependency parsing tries to optimize the probability of a corpus by modifying the dependency model that was presumably used to generate the corpus. In this article we explore a different view in which a dependency structure is among other things a partial order on the nodes in terms of centrality or saliency. Under this assumption we model the partial order directly and der...

In this paper, we present a simple and effective fine-grained feature generation scheme for dependency parsing. We focus on the problem of grammar representation, introducing fine-grained features by splitting various POS tags to different degrees using HowNet hierarchical semantic knowledge. To prevent the oversplitting, we adopt a threshold-constrained bottomup strategy to merge the derived s...