GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 4
| Item Name: | GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 4 |
| Author(s): | Xuansong Li, Stephen Grimes, Stephanie Strassel |
| LDC Catalog No.: | LDC2015T18 |
| ISBN: | 1-58563-732-7 |
| ISLRN: | 161-465-005-066-8 |
| DOI: | https://doi.org/10.35111/jt1b-sv88 |
| Release Date: | September 15, 2015 |
| Member Year(s): | 2015 |
| DCMI Type(s): | Text |
| Data Source(s): | broadcast conversation, broadcast news |
| Project(s): | GALE |
| Application(s): | automatic content extraction, content-based retrieval, machine translation, tagging |
| Language(s): | Mandarin Chinese, English, Chinese |
| Language ID(s): | cmn, eng, zho |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2015T18 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Li, Xuansong, Stephen Grimes, and Stephanie Strassel. GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 4 LDC2015T18. Web Download. Philadelphia: Linguistic Data Consortium, 2015. |
| Related Works: | View |
Introduction
GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 4 was developed by the Linguistic Data Consortium (LDC) and contains 243,038 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.
Other releases available in this series are:
- GALE Chinese-English Word Alignment and Tagging Training Part 1 -- Newswire and Web (LDC2012T16)
- GALE Chinese-English Word Alignment and Tagging Training Part 2 -- Newswire (LDC2012T20)
- GALE Chinese-English Word Alignment and Tagging Training Part 3 -- Web (LDC2012T24)
- GALE Chinese-English Word Alignment and Tagging Training Part 4 -- Web (LDC2013T05)
- GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 1 (LDC2013T23)
- GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 2 (LDC2014T25)
- GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 3 (LDC2015T04)
Data
This release consists of Chinese source broadcast conversation (BC) and broadcast news (BN) programming collected by LDC in 2008 and 2009. The distribution by genre, words, character tokens and segments appears below:
| Language | Genre | Files | Words | CharTokens | Segments |
| Chinese | BC | 69 | 67,782 | 101,674 | 2,276 |
| Chinese | BN | 29 | 94,242 | 141,364 | 3,152 |
| Total | 98 | 162,024 | 243,038 | 5,428 |
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 eight 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
Please view the following sample.
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.