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It's Been Said Before

By Orin Hargraves

It's Been Said Before "examines why certain phrases become clichés and why they should be avoided -- or why they still have life left in them."

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Sounds Fascinating

By J. C. Wells

How do you pronounce biopic, synod, and Breughel? - and why? Do our cake and archaic sound the same? Where does the stress go in stalagmite? What's odd about the word epergne? As a finale, the author writes a letter to his 16-year-old self.

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.

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