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Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Deep Convolutional Neural Model for Character-Based Chinese Word Segmentation

Authors : Zhipeng Xie, Junfeng Hu

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

This paper proposes a deep convolutional neural model for character-based Chinese word segmentation. It first constructs position embeddings to encode unigram and bigram features that are directly related to single positions in input sentence, and then adaptively builds up hierarchical position representations with a deep convolutional net. In addition, a multi-task learning strategy is used to further enhance this deep neural model by treating multiple supervised CWS datasets as different tasks. Experimental results have shown that our neural model outperforms the existing neural ones, and the model equipped with multi-task learning has successfully achieved state-of-the-art F-score performance for standard benchmarks: 0.964 on PKU dataset and 0.978 on MSR dataset.

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Footnotes
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Metadata
Title
A Deep Convolutional Neural Model for Character-Based Chinese Word Segmentation
Authors
Zhipeng Xie
Junfeng Hu
Copyright Year
2018
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_32

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