Introduction
GALE Chinese-English Word Alignment and Tagging Training Part 3 -- Web
was developed by the Linguistic Data Consortium (LDC) and contains 154,541 tokens of word aligned Chinese and
English parallel text enriched with linguistic tags. This material was used
as training data in the DARPA
GALE (Global Autonomous Language Exploitation) program.
Some approaches to statistical machine translation include the incorporation
of linguistic knowledge in word aligned text as a means to improve automatic
word alignment and machine translation quality. This is accomplished with two
annotation schemes: alignment and tagging. Alignment identifies minimum translation
units and translation relations by using minimum-match and attachment annotation
approaches. A set of word tags and alignment link tags are designed in the tagging
scheme to describe these translation units and relations. Tagging adds contextual,
syntactic and language-specific features to the alignment annotation.
GALE Chinese-English Word Alignment and Tagging Training Part 1 -- Newswire and Web
(LDC2012T16) and GALE Chinese-English Word Alignment and Tagging Training Part 2 -- Newswire
(LDC2012T20) are also available through LDC.
Data
This release consists of Chinese source web data (newsgroup, weblog) collected
by LDC in 2008 and 2009. The distribution by words, character tokens and segments appears
below:
| Language | Files | Words | CharTokens | Segments |
| Chinese | 1249 | 103027 | 154541 | 4842 |
Note that all token counts are based on the Chinese data only. One token is equivalent
to one character and one word is equivalent to 1.5 characters.
The Chinese word alignment tasks consisted of the following components:
- Identifying, aligning, and tagging 8 different types of links
- Identifying, attaching, and tagging local-level unmatched words
- Identifying and tagging sentence/discourse-level unmatched words
- Identifying and tagging all instances of Chinese 的(DE) except when they
were a part of a semantic link.
Samples
Sponsorship
This work was supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
Updates
None at this time.
Content Copyright
Portions © 2008, 2009, 2012 Trustees of the University of Pennsylvania
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