LORELEI Vietnamese Representative Language Pack
Item Name: | LORELEI Vietnamese Representative Language Pack |
Author(s): | Jennifer Tracey, Stephanie Strassel, David Graff, Jonathan Wright, Song Chen, Neville Ryant, Seth Kulick, Kira Griffitt, Dana Delgado, Michael Arrigo |
LDC Catalog No.: | LDC2020T17 |
ISBN: | 1-58563-933-8 |
ISLRN: | 257-294-146-794-3 |
DOI: | https://doi.org/10.35111/B7KZ-9804 |
Release Date: | August 17, 2020 |
Member Year(s): | 2020 |
DCMI Type(s): | Software, Text |
Data Source(s): | discussion forum, newswire, web collection, weblogs |
Project(s): | LORELEI |
Application(s): | cross-language transfer, entity extraction, information extraction, machine translation |
Language(s): | Vietnamese, English |
Language ID(s): | vie, eng |
License(s): |
LDC User Agreement for Non-Members |
Online Documentation: | LDC2020T17 Documents |
Licensing Instructions: | Subscription & Standard Members, and Non-Members |
Citation: | Tracey, Jennifer, et al. LORELEI Vietnamese Representative Language Pack LDC2020T17. Web Download. Philadelphia: Linguistic Data Consortium, 2020. |
Related Works: | View |
Introduction
LORELEI Vietnamese Representative Language Pack consists of Vietnamese monolingual text, Vietnamese-English parallel text, annotations, supplemental resources and related software tools developed by the Linguistic Data Consortium for the DARPA LORELEI program.
The LORELEI (Low Resource Languages for Emergent Incidents) program was concerned with building human language technology for low resource languages in the context of emergent situations like natural disasters or disease outbreaks. Linguistic resources for LORELEI include Representative Language Packs and Incident Language Packs for over two dozen low resource languages, comprising data, annotations, basic natural language processing tools, lexicons and grammatical resources. Representative languages were selected to provide broad typological coverage, while incident languages were selected to evaluate system performance on a language whose identity was disclosed at the start of the evaluation.
Data
Vietnamese is spoken mainly in Vietnam, as well as in Guangxi Province in southern China, Cambodia and Laos. Data was collected in the following genres: discussion forum, news, reference, social network, and weblogs. Both monolingual text collection and parallel text creation involved a combination of manual and automatic methods.
Data volumes are as follows:
- Over 172 million words of Vietnamese monolingual text, approximately 325,000 words of which were translated into English
- 106,000 Vietnamese words translated from English data
- 1.9 million words of found parallel text
Approximately 75,000 words were annotated for named entities and up to 25,000 words contain additional annotation, including situation frames (identifying entities, needs and issues) and entity linking and detection.
Lexical resources and software tools are also included in this release. The tools recreate original source data from the processed XML material and condition text data users download from Twitter.
Monolingual and parallel text are presented in XML with associated dtds. Annotation data is presented as tab delimited files. All text is UTF-8 encoded. The knowledge base for entity linking annotation for this corpus and all LORELEI Representative Language and Incident Language Packs is available separately as LORELEI Entity Detection and Linking Knowledge Base (LDC2020T10).
Acknowledgement
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-15-C-0123. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA.
Samples
Please view the following samples:
- Vietnamese LTF XML
- Vietnamese PSM XML
- English LTF XML
- English PSM XML
- Simple Named Entity Annotation (XML)
- Full Named Entity Annotation (XML)
- Simple Semantic Annotation (XML)
- Situation Frame Annotation (TXT)
- Noun Phrase Chunking Annotation (XML)
Updates
None at this time.