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The present work advances the accu- racy and training speed of discrimina- tive parsing. Our discriminative parsing method has no generative component, yet surpasses a generative baseline on con- stituent …...

We present a constituent parsing-based reordering technique that improves the performance of the state-of-the-art Eng- lish-to-Japanese phrase translation sys- tem that includes distortion models by 4.76 BLEU points. The phrase …...

AbstractWe investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and …...

We compare two different methods in domain adaptation applied to constituent parsing: parser combination and co- training, each used to transfer information from the source domain of news to the …...

We present a novel, structured language model - Supertagged Dependency Language Model to model the syntactic dependencies between words. The goal is to identify ungrammatical hypotheses from a set of …...

We introduce three techniques for improv- ing constituent parsing for morphologi- cally rich languages. We propose a novel approach to automatically find an optimal preterminal set by clustering morphologi- cal …...

We propose a spectral approach for un- supervised constituent parsing that comes with theoretical guarantees on latent struc- ture recovery. Our approach is grammar- less – we directly learn the …...

We present a transition-based arc-eager model to parse spinal trees, a dependency- based representation that includes phrase- structure information in the form of con- stituent spines assigned to tokens. As …...

AbstractTo ensure portability of NLP systems across multiple domains, existing treebanks are often extended by adding trees from interesting domains that were not part of the initial annotation effort. In …...

AbstractWe present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique …...

Dynamic oracle training has shown sub- stantial improvements for dependency parsing in various settings, but has not been explored for constituent parsing. The present article introduces a dynamic ora- cle …...

. The article introduces a transition-based constituent parsing method relying on a deep-learning weighting scheme. This weighting method is compared to a structured percep- tron, which is a more traditional …...

AbstractTransition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a …...

AbstractSequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high …...

AbstractHead-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating …...

AbstractThe most straightforward approach to joint word segmentation (WS), part-of-speech (POS) tagging, and constituent parsing is converting a word-level tree into a char-level tree, which, however, leads to two severe …...