Efficient Parsing for Head-Split Dependency Trees

Efficient Parsing for Head-Split Dependency Trees

Head splitting techniques have been successfully exploited to improve the asymptotic runtime of parsing algorithms for projective dependency trees, under the arc-factored model. In this article we extend these techniques to a class of non-projective dependency trees, called well-nested dependency trees with block-degree at most 2, which has been previously investigated in the literature. We def...

Efficient Incremental Dependency Parsing

This paper describes an e cient method of incremental dependency parsing based on phrase structure grammar with the dependency relation. The reachability relation between syntactic categories is utilized for connecting a head word with a dependent word simultaneously with the inputs. The method does not need to construct the whole parse tree of an initial fragment on the word-by-word basis, and...

Dependency Parsing as Head Selection

Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call DENSE (as shorthand for Dependency Neural Selection) produces a distribution over possible heads for each word using features obtained from a bidi...

An Efficient Algorithm for Projective Dependency Parsing

Dependency Parsing with Efficient Feature Extraction

The fastest parsers currently can parse an average sentence in up to 2.5ms, a considerable improvement, since most of the older accuracy-oriented parsers parse only few sentences per second. It is generally accepted that the complexity of a parsing algorithm is decisive for the performance of a parser. However, we show that the most time consuming part of processing is feature extraction and th...