DOI: 10.35111/w1ev-pj90
title: GALE Chinese-English Word Alignment and Tagging Training Part 3 -- Web

description: <h3>Introduction</h3> <p>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 <a href="http://projects.ldc.upenn.edu/gale/index.html" rel="nofollow">DARPA GALE</a> (Global Autonomous Language Exploitation) program.</p> <p> 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. </p> <p>GALE Chinese-English Word Alignment and Tagging Training Part 1 -- Newswire and Web (<a href="http://catalog.ldc.upenn.edu/LDC2012T16" rel="nofollow">LDC2012T16</a>) and GALE Chinese-English Word Alignment and Tagging Training Part 2 -- Newswire (<a href="http://catalog.ldc.upenn.edu/LDC2012T20" rel="nofollow">LDC2012T20</a>) are also available through LDC.</p> <h3>Data</h3> <p>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: </p> <table> <tr> <td>Language</td> <td>Files</td> <td>Words</td> <td>CharTokens</td> <td>Segments</td> </tr> <tr> <td>Chinese</td> <td>1249</td> <td>103027</td> <td>154541</td> <td>4842</td> </tr> </table><p>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.</p> <p> The Chinese word alignment tasks consisted of the following components: </p> <ul> <li>Identifying, aligning, and tagging 8 different types of links</li> <li>Identifying, attaching, and tagging local-level unmatched words</li> <li>Identifying and tagging sentence/discourse-level unmatched words</li> <li>Identifying and tagging all instances of Chinese 的 (DE) except when they were a part of a semantic link.</li> </ul><h3>Samples</h3> <ul> <li><a href="./desc/addenda/LDC2012T24.cmn.raw.txt" rel="nofollow">Chinese raw source sample</a></li> <li><a href="./desc/addenda/LDC2012T24.cmn.tkn.txt" rel="nofollow">Chinese character tokenized sample</a></li> <li><a href="./desc/addenda/LDC2012T24.eng.raw.txt" rel="nofollow">English raw translation sample</a></li> <li><a href="./desc/addenda/LDC2012T24.eng.tkn.txt" rel="nofollow">English tokenized sample</a></li> <li><a href="./desc/addenda/LDC2012T24.wa.txt" rel="nofollow">Character-based word alignment sample</a></li> </ul><h3>Sponsorship</h3> <p>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.</p> <h3>Updates</h3> <p> None at this time. </p>  </br>
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