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2018 | OriginalPaper | Buchkapitel

Bidirectional Deep Learning of Context Representation for Joint Word Segmentation and POS Tagging

verfasst von : Prachya Boonkwan, Thepchai Supnithi

Erschienen in: Advanced Computational Methods for Knowledge Engineering

Verlag: Springer International Publishing

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Abstract

Word segmentation and POS tagging are crucial steps for natural language processing. Though deep learning facilitates learning a joint model without feature engineering, it still suffers from unreliable word embedding when words are rare or unknown. We introduce two-level backoff models to which morphological information and character-level contexts are integrated. Experimental results on Thai and Chinese show that our backoff models improve the accuracy of both tasks and excels in OOV recovery.

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Vorheriges Kapitel An Early-Biologisation Process to Improve the Acceptance of Biomimetics in Organizations

Nächstes Kapitel A Model for a Computing Cluster with Two Asynchronous Servers

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Titel: Bidirectional Deep Learning of Context Representation for Joint Word Segmentation and POS Tagging
verfasst von: Prachya Boonkwan
Thepchai Supnithi
Verlag: Springer International Publishing
Buch: Advanced Computational Methods for Knowledge Engineering
Print ISBN: 978-3-319-61910-1

Electronic ISBN: 978-3-319-61911-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-61911-8_17

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