A method is described to incorporate bilexical preferences between phrase heads, such as selection restrictions, in a MaximumEntropy parser for Dutch. The bilexical preferences are modelled as association rates which are determined on the basis of a very large parsed corpus (about 500M words). We show that the incorporation of such selftrained preferences improves parsing accuracy significantly.

Using constraints in robust parsing seems to have what we call "robust parsing paradox". Preference Sem,'ultics and Coimecfionisln both offered a promising approach to fins problem. However, Prefelence Semantics has not addressed the problem of how to make flfll use of syntactic constraints, and Connectiouism has some inherent difficulties of its OWll which prevent it producing a practical syst...

A method is described to incorporate bilexical preferences between phrase heads, such as selection restrictions, in a MaximumEntropy parser for Dutch. The bilexical preferences are modelled as association rates which are determined on the basis of a very large parsed corpus (about 500M words). We show that the incorporation of such selftrained preferences improves parsing accuracy significantly.

A method is described to incorporate bilexical preferences between phrase heads, such as selection restrictions, in a Maximum-Entropy parser for Dutch. The bilexical preferences are model-led as association rates which are determined on the basis of a very large parsed corpus (about 500M words). The preferences are incorporated in the Maximum Entropy framework as auxiliary distributions, using ...

Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of-Speech (POS) tagging is a prerequisite for dependency parsing. Generally, dependency parsers do the POS tagging task along with dependency parsing in a pipeline mode. Unfortunately, in pipel...

A hybrid approach to automatic derivation of class-based selectional preferences is proposed. A lexicon of selectional preferences can assist in handling several forms of ambiguity, a major problem for MT. The approach combines knowledge-rich parsing and lexicons, with statistics and corpus data. We illustrate the use of a selectional preference lexicon for anaphora resolution.

Hierarchical lexicon structures are not only of great importance for the nonredundant representation of lexical information, they may also contribute to the efficiency of the actual processing of natural language. Two parsing techniques that render the parsing process efficient are presented. Windowing is a technique for incrementally accessing the hierarchical lexicon. Lexical preferencing imp...

In Japanese dependency parsing, Kudo’s relative preference-based method (Kudo and Matsumoto, 2005) outperforms both deterministic and probabilistic CKY-based parsing methods. In Kudo’s method, for each dependent word (or chunk) a loglinear model estimates relative preference of all other candidate words (or chunks) for being as its head. This cannot be considered in the deterministic parsing me...

We present a model of global processing difficulty in human parsing. This model is based on a probabilistic context-free grammar and is trained on a realistic corpus sample. It achieves broad coverage and good parsing accuracy on unseen text, and its predictions are significantly correlated with experimental data on word order preferences in German. The model makes predictions about the differe...

This paper presents the results of a quantitative study adjuncts in Airbus corpus carried out within theoretical framework Role and Reference Grammar (RRG). We describe positional behaviour these peripheral constituents Layered Structure Clause postulate scales preferences, based on frequency distribution, controlled natural language (CNL). The obtained were compared with previous adjunct prefe...