The last few years have seen a surge in the number of accurate, fast, publicly available dependency parsers. At the same time, the use of dependency parsing in NLP applications has increased. It can be difficult for a non-expert to select a good “off-the-shelf” parser. We present a comparative analysis of ten leading statistical dependency parsers on a multi-genre corpus of English. For our ana...

Preliminary experiments presented in this paper consist in the induction and evaluation of a dependency parser for Polish. We train data-driven dependency models with publicly available parser-generation systems (MaltParser and MSTParser) given a converted dependency structure bank for Polish. Induced Polish dependency parsers are evaluated against a set of gold standard dependency structures u...

This master’s thesis describes a deterministic dependency parser using a memorybased learning approach to parse unrestricted English text. A converter transforms the Wall Street Journal section of the Penn Treebank to an intermediate dependency representation which is used to train the parser using the TiMBL (Daelemans, Zavrel, Sloot, & Bosch, 2003) library. The output of the parser is labeled ...

In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English ...

We investigate the effect of unlexicalization in a dependency parser for variable word order languages and propose an unlexicalized parser which can utilize some contextual information in order to achieve performance comparable to that of lexicalized parsers. Unlexicalization of an early dependency parser makes performance decrease by 3.6%. However, when we modify the unlexicalized parser into ...

This paper proposes a left-corner parser which can identify nonlocal dependencies. Our parser integrates nonlocal dependency identification into a transition-based system. We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies. An experimental result demonstrates that our parser achieves a good balance between constituent parsing and...

Dependency parsers are critical components within many NLP systems. However, currently available dependency parsers each exhibit at least one of several weaknesses, including high running time, limited accuracy, vague dependency labels, and lack of nonprojectivity support. Furthermore, no commonly used parser provides additional shallow semantic interpretation, such as preposition sense disambi...

Dependency parsing has been a longstanding task in NLP with a recent boost in performance thanks to neural network models. However, most dependency parsers are monolingual–a single parser is trained per language–and utilize transitionbased systems that are limited to local information. We utilize a novel transitionbased system, arc-swift, proposed in [1] that incorporates both local and global ...

We explore the use of a partially annotated corpus to build a dependency parser for Japanese. We examine two types of partially annotated corpora. It is found that a parser trained with a corpus that does not have any grammatical tags for words can demonstrate an accuracy of 87.38%, which is comparable to the current state-of-the-art accuracy on the Kyoto University Corpus. In contrast, a parse...