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Erschienen in:
Supervised Deep Learning for Hierarchical Image Data Retrieval Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Latent Variable CRF Model for Labeling Prediction

verfasst von : Jerry Chun-Wei Lin, Jimmy Ming-Tai Wu, Yinan Shao, Matin Pirouz, Binbin Zhang

Erschienen in: Multidisciplinary Social Networks Research

Verlag: Springer Singapore

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Abstract

A latent variable conditional random fields (CRF) model is proposed to improve sequence labeling, which utilizes the BIO encoding schema as latent variable to capture the latent structure of hidden variables and observation data. The proposed model automatically selects the best encoding schema for each given input sequence. Through experimentation, it is demonstrated that the proposed model unveils the latent variable while performing robustly on sequence-labeling prediction tasks.

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Vorheriges Kapitel An Effective BI-encoded Schema for Mention Extraction
Nächstes Kapitel Exploring the Influencing Factors of Live-Streaming Viewers’ Participation Intention from the Perspective of Source Credibility Model and Cognitive Load - An Example of Mobile Device Users
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Metadaten
Titel
A Latent Variable CRF Model for Labeling Prediction
verfasst von
Jerry Chun-Wei Lin
Jimmy Ming-Tai Wu
Yinan Shao
Matin Pirouz
Binbin Zhang
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Multidisciplinary Social Networks Research

Print ISBN: 978-981-15-1757-0

Electronic ISBN: 978-981-15-1758-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-15-1758-7_6