We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, “head-ordered dependency trees,” shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we ...

We present a dependency-driven parser that parses both dependency structures and constituent structures. Constituency representations are automatically transformed into dependency representations with complex arc labels, which makes it possible to recover the constituent structure with both constituent labels and grammatical functions. We report a labeled attachment score close to 90% for depen...

We present a novel transition-based, greedy dependency parser which implements a flexible mix of bottom-up and top-down strategies. The new strategy allows the parser to postpone difficult decisions until the relevant information becomes available. The novel parser has a ∼12% error reduction in unlabeled attachment score over an arc-eager parser, with a slow-down factor

This article presents a dependency parsing scheme using an extended finite-state approach. The parser augments input representation with “channels” so that links representing syntactic dependency relations among words can be accommodated and iterates on the input a number of times to arrive at a fixed point. Intermediate configurations violating various constraints of projective dependency repr...

We describe a practical parser for unrestricted dependencies. The parser creates links between words and names the links according to their syntactic functions. We rst describe the older Constraint Grammar parser where many of the ideas come from. Then we proceed to describe the central ideas of our new parser. Finally, the parser is evaluated.

NLP systems will be more portable among medical domains if acquisition of semantic lexicons can be facilitated. We are pursuing lexical acquisition through the syntactic relationships of words in medical corpora. Therefore we require a syntactic parser which is flexible, portable, captures head-modifier pairs and does not require a large training set. We have designed a dependency grammar parse...

MICA is a dependency parser which returns deep dependency representations, is fast, has state-of-the-art performance, and is freely available.

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed algorithm Networks’ sequentia...

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal section of the Penn Treebank, the parser achieves a maximum attachment score of 87.1%. Unlike most previous systems, the parser produces labeled dependency graphs, using as arc labels a combination of bracket labels...

In this paper, we propose using a ”bootstrapping” method for constructing a dependency treebank of Arabic tweets. This method uses a rule-based parser to create a small treebank of one thousand Arabic tweets and a data-driven parser to create a larger treebank by using the small treebank as a seed training set. We are able to create a dependency treebank from unlabelled tweets without any manua...