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An Improved Class-Center Method for Text Classification Using Dependencies and WordNet Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Cross-lingual Neural Vector Conceptualization

Authors : Lisa Raithel, Robert Schwarzenberg

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Recently, Neural Vector Conceptualization (NVC) was proposed as a means to interpret samples from a word vector space. For NVC, a neural model activates higher order concepts it recognizes in a word vector instance. To this end, the model first needs to be trained with a sufficiently large instance-to-concept ground truth, which only exists for a few languages. In this work, we tackle this lack of resources with word vector space alignment techniques: We train the NVC model on a high resource language and test it with vectors from an aligned word vector space of another language, without retraining or fine-tuning. A quantitative and qualitative analysis shows that the NVC model indeed activates meaningful concepts for unseen vectors from the aligned vector space. NVC thus becomes available for low resource languages for which no appropriate concept ground truth exists.

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Footnotes
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Metadata
Title
Cross-lingual Neural Vector Conceptualization
Authors
Lisa Raithel
Robert Schwarzenberg
Copyright Year
2019
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-32235-9

Electronic ISBN: 978-3-030-32236-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32236-6_59

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