Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info

New from Oxford University Press!


Evolutionary Syntax

By Ljiljana Progovac

This book "outlines novel and testable hypotheses, contains extensive examples from many different languages" and is "presented in accessible language, with more technical discussion in footnotes."

New from Cambridge University Press!


The Making of Vernacular Singapore English

By Zhiming Bao

This book "proposes a new theory of contact-induced grammatical restructuring" and "offers a new analytical approach to New English from a formal or structural perspective."

Academic Paper

Title: Dependency-based n-gram models for general purpose sentence realisation
Author: Yuqing Guo
Institution: Toshiba (China) Research and Development Center
Author: Haifeng Wang
Institution: Baidu
Author: Josef Van Genabith
Email: click here TO access email
Institution: Dublin City University
Linguistic Field: Computational Linguistics; Semantics; Syntax
Subject Language: Chinese, Mandarin
Abstract: This paper presents a general-purpose, wide-coverage, probabilistic sentence generator based on dependency n-gram models. This is particularly interesting as many semantic or abstract syntactic input specifications for sentence realisation can be represented as labelled bi-lexical dependencies or typed predicate-argument structures. Our generation method captures the mapping between semantic representations and surface forms by linearising a set of dependencies directly, rather than via the application of grammar rules as in more traditional chart-style or unification-based generators. In contrast to conventional n-gram language models over surface word forms, we exploit structural information and various linguistic features inherent in the dependency representations to constrain the generation space and improve the generation quality. A series of experiments shows that dependency-based n-gram models generalise well to different languages (English and Chinese) and representations (LFG and CoNLL). Compared with state-of-the-art generation systems, our general-purpose sentence realiser is highly competitive with the added advantages of being simple, fast, robust and accurate.


This article appears IN Natural Language Engineering Vol. 17, Issue 4, which you can READ on Cambridge's site or on LINGUIST .

Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page