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A Comparison Among Significance Tests and Other Feature Building Methods for Sentiment Analysis: A First Study Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Supervised Domain Adaptation via Label Alignment for Opinion Expression Extraction

Authors : Yifan Zhang, Arjun Mukherjee, Fan Yang

Published in: Computational Linguistics and Intelligent Text Processing

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a supervised domain adaptation technique for opinion expression extraction task. The technique generates low dimensional projections that can improve the performance of a sequence model (e.g. CRF) in the target domain by align features with the true label sequence. We test our methods on product reviews and observe significant improvement in performance in comparison to baseline methods.

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previous chapter A Comparative Study of Target-Based and Entity-Based Opinion Extraction
next chapter Comment Relevance Classification in Facebook
Footnotes
2
Many of the Source Only baselines have a \(F_1\) too low to be visible in this bar chart, please refer to Table 2 for their actual readings.
 
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Metadata
Title
Supervised Domain Adaptation via Label Alignment for Opinion Expression Extraction
Authors
Yifan Zhang
Arjun Mukherjee
Fan Yang
Copyright Year
2018
Book
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-77115-1

Electronic ISBN: 978-3-319-77116-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77116-8_17

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