Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to benefit each other. This paper presents a universal framework for transfer parsing across multi-typed treebanks with deep multi-task learning. We consider two kinds of treebanks as...

This paper addresses the problem of segmenting German texts into minimal discourse units, as they are needed, for example, in RST-based discourse parsing. We discuss relevant variants of the problem, introduce the design of our annotation guidelines, and provide the results of an extensive interannotator agreement study of the corpus. Afterwards, we report on our experiments with three automati...

Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain syntactic knowledge that is potential to benefit each other. This paper presents an universal framework for exploiting these multi-typed treebanks to improve parsing with deep multitask learning. We consider two kinds of treeban...

Most previous approaches to syntactic parsing of Chinese rely on a preprocessing step of word segmentation, thereby assuming there was a clearly defined boundary between morphology and syntax in Chinese. We show how this assumption can fail badly, leading to many out-of-vocabulary words and incompatible annotations. Hence in practice the strict separation of morphology and syntax in the Chinese...

Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because C...

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semantically equivalent syntactic structures, and (3) predict predicate-argument relations between...

Extensible Dependency Grammar (XDG) is new, modular grammar formalism for natural language. An XDG analysis is a multi-dimensional dependency graph, where each dimension represents a different aspect of natural language, e.g. syntactic function, predicate-argument structure, information structure etc. Thus, XDG brings together two recent trends in computational linguistics: the increased applic...

The application of the Meaning ⇔ Text Theory to Spanish parsing is presented. This formalism is based on dependency grammars. The combinatorial dictionary of this method is employed for the syntactic analysis; it consists of patterns for words, mainly verbs, where all its valences and the way they are realized are described. In this method, no fixed word order in the sentence is considered so i...

The paper reports on the recent forum RU-EVAL ‒ a new initiative for evaluation of Russian NLP resources, methods and toolkits. It started in 2010 with evaluation of morphological parsers, and the second event RU-EVAL 2012 (2011-2012) focused on syntactic parsing. Eight participating IT companies and academic institutions submitted their results for corpus parsing. We discuss the results of thi...

Abstract We present Expected Statistic Regulariza tion (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi- supervised learning on low-resource datasets. study ESR in the context of cross-lingual transfer syntactic analysis (POS tagging and labeled dependency parsing) several classes statistic functions bear beha...