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Erschienen in:
fMRI Semantic Category Decoding Using Linguistic Encoding of Word Embeddings Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Topic-Bigram Enhanced Word Embedding Model

verfasst von : Qi Yang, Ruixuan Li, Yuhua Li, Qilei Liu

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a novel model which exploits the topic relevance to enhance the word embedding learning. We attempt to leverage the hidden topic-bigram model to build topic relevance matrices, then learn the Topic-Bigram Word Embedding (TBWE) by aggregating the context as well as corresponding topic-bigram information. The topic relevance weights are updated with word embeddings simultaneously during the training process. To verify the validity and accuracy of the model, we conduct experiments on word analogy task and word similarity task. The results show that the TBWE model can achieve the better performance in both two tasks.

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Vorheriges Kapitel Event Causality Identification by Modeling Events and Relation Embedding
Nächstes Kapitel Hybridized Character-Word Embedding for Korean Traditional Document Translation
Fußnoten
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Metadaten
Titel
Topic-Bigram Enhanced Word Embedding Model
verfasst von
Qi Yang
Ruixuan Li
Yuhua Li
Qilei Liu
Copyright-Jahr
2018
Buch
Neural Information Processing

Print ISBN: 978-3-030-04181-6

Electronic ISBN: 978-3-030-04182-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-04182-3_7