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Building a Collation Element Table for a Large Chinese Character Set in YES Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Three-Layered Collocation Extraction Tool and Its Application in China English Studies

Authors : Jingxiang Cao, Dan Li, Degen Huang

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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Abstract

We design a three-layered collocation extraction tool by integrating syntactic and semantic knowledge and apply it in China English studies. The tool first extracts peripheral collocations in the frequency layer from dependency triples, then extracts semi-peripheral collocations in the syntactic layer by association measures, and last extracts core collocations in the semantic layer with a similar word thesaurus. The syntactic constraints filter out much noise from surface co-occurrences, and the semantic constraints are effective in identifying the very “core” collocations. The tool is applied to automatically extract collocations from a large corpus of China English we compile to explore how China English as a variety of English is nativilized. Then we analyze similarities and differences of the typical China English collocations of a group of verbs. The tool and results can be applied in the compilation of language resources for Chinese-English translation and corpus-based China studies.

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previous chapter Incorporating Word Clustering into Complex Noun Phrase Identification
next chapter The Designing and Construction of Domain-oriented Vietnamese-English-Chinese FrameNet
Footnotes
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Metadata
Title
A Three-Layered Collocation Extraction Tool and Its Application in China English Studies
Authors
Jingxiang Cao
Dan Li
Degen Huang
Copyright Year
2015
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25816-4_4

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