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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 Neural Network Based Translation Constrained Reranking Model for Chinese Dependency Parsing

Authors : Miaohong Chen, Baobao Chang, Yang Liu

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

Publisher: Springer International Publishing

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Abstract

Bilingual dependency parsing aims to improve parsing performance with the help of bilingual information. While previous work have shown improvements on either or both sides, most of them mainly focus on designing complicated features and rely on golden translations during training and testing. In this paper, we propose a simple yet effective translation constrained reranking model to improve Chinese dependency parsing. The reranking model is trained using a max-margin neural network without any manually designed features. Instead of using golden translations for training and testing, we relax the restrictions and use sentences generated by a machine translation system, which dramatically extends the scope of our model. Experiments on the translated portion of the Chinese Treebank show that our method outperforms the state-of-the-art monolingual Graph/Transition-based parsers by a large margin (UAS).

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previous chapter Graph-Based Dependency Parsing with Recursive Neural Network
next chapter Distantly Supervised Neural Network Model for Relation Extraction
Footnotes
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Metadata
Title
A Neural Network Based Translation Constrained Reranking Model for Chinese Dependency Parsing
Authors
Miaohong Chen
Baobao Chang
Yang Liu
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_20

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