Climbing the tower of babel: Unsupervised multilingual learning
For centuries, scholars have explored the deep links among human languages. In this paper, we present a class of probabilistic models that use these links as a form of naturally occurring supervision. These models allow us to substantially improve performance for core text processing tasks, su...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Omnipress
2011
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| Online Access: | http://hdl.handle.net/1721.1/61698 https://orcid.org/0000-0002-2921-8201 |
| Summary: | For centuries, scholars have explored the deep
links among human languages. In this paper,
we present a class of probabilistic models
that use these links as a form of naturally
occurring supervision. These models allow
us to substantially improve performance for
core text processing tasks, such as morphological
segmentation, part-of-speech tagging,
and syntactic parsing. Besides these traditional
NLP tasks, we also present a multilingual
model for the computational decipherment
of lost languages. |
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