Neural name tagging for low-resource languages
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Authors
Zhang, Boliang
Issue Date
2019-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
Abstract
At last, we investigate training LL name taggers without using any LL annotation. We transfer a name tagger that trained on HL annotations to a LL name tagger via two unsupervised approaches: 1) cross-lingual word embedding where we align monolingual word embedding of HL and LL into a shared space, and 2) cross-lingual language model where instead of aligning word embedding, we project the contextualized word embedding (language model) of HL and LL into a shared space.
Description
August 2019
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY