We propose a solution to the annotation bottleneck for statistical parsing, by exploiting the lexicalized nature of Combinatory Categorial Grammar (CCG). The parsing model uses predicate-argument dependencies for training, which are derived from sequences of CCG lexical categories rather than full derivations. A simple method is used for extracting dependencies from lexical category sequences, ...

Many NLP tasks such as question answering and knowledge acquisition are tightly dependent on dependency parsing. Dependency parsing accuracy is always decisive for the performance of subsequent tasks. Therefore, reducing dependency parsing errors or selecting high quality dependencies is a primary issue. In this paper, we present a supervised approach for automatically selecting high quality de...

In this article, we present and evaluate an approach to the combination of a grammardriven and a data-driven parser which exploits machine learning for the acquisition of syntactic analyses guided by both parsers. We show how conversion of LFG output to dependency representation allows for a technique of parser stacking, whereby the output of the grammar-driven parser supplies features for a da...

Many NLP systems use dependency parsers as critical components. Jonit learning parsers usually achieve better parsing accuracies than two-stage methods. However, classical joint parsing algorithms significantly increase computational complexity, which makes joint learning impractical. In this paper, we proposed an efficient dependency parsing algorithm that is capable of capturing multiple edge...

This paper presents a new implementation of the multipurpose set of NLP tools for Polish, made available online in a common web service framework. The tool set comprises a morphological analyzer, a tagger, a named entity recognizer, a dependency parser, a constituency parser and a coreference resolver. Additionally, a web application offering chaining capabilities and a common BRAT-based presen...

This article evaluates the extension of a dependency parser that performs joint syntactic analysis and multiword expression identification. We show that, given sufficient training data, the parser benefits from explicit multiword information and improves overall labeled accuracy score in eight of the ten evaluation cases.

In this short paper, an off-the-shelf maximum entropy-based POS-tagger is used as a partial parser to improve the accuracy of an extremely fast linear time dependency parser that provides state-of-the-art results in multilingual unlabeled POS sequence parsing.

In this paper, I describe a hybrid dependency tree parser for Sanskrit sentences improving on a purely lexical parsing approach through simple syntactic rules and grammatical information. The performance of the parser is demonstrated on a group of sentences from epic literature.

This paper reports some experiments that compare the accuracy and performance of two stochastic parsing systems. The currently popular Collins parser is a shallow parser whose output contains more detailed semanticallyrelevant information than other such parsers. The XLE parser is a deep-parsing system that couples a Lexical Functional Grammar to a loglinear disambiguation component and provide...

Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding many graph learning tasks. Inspired by the success of GNNs, we investigate improving parsing with encoded multi-layer GNNs. Experiments are conducted on SemEval 2015 Task...