LORELEI Oromo Incident Language Pack

Item Name: LORELEI Oromo Incident Language Pack
Author(s): Jennifer Tracey, David Graff, Stephanie Strassel, Michael Arrigo, Jonathan Wright, Ann Bies
LDC Catalog No.: LDC2020T11
ISBN: 1-58563-929-X
ISLRN: 067-446-898-117-9
Release Date: May 15, 2020
Member Year(s): 2020
DCMI Type(s): Text, Software
Data Source(s): web collection, newswire, weblogs, newsgroups, discussion forum, religious texts
Project(s): LORELEI
Application(s): information extraction, entity extraction, machine translation, cross-language transfer
Language(s): Oromo, English
Language ID(s): orm, eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2020T11 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Tracey, Jennifer, et al. LORELEI Oromo Incident Language Pack LDC2020T11. Web Download. Philadelphia: Linguistic Data Consortium, 2020.
Related Works: View

Introduction

LORELEI Oromo Incident Language Pack was developed by the Linguistic Data Consortium and is comprised of approximately 3.9 million words of Oromo monolingual text, 25,000 words of English monolingual text, 135,000 words of parallel and comparable Oromo-English text, and 50,000 words of data annotated for Entity Discovery and Linking and Situation Frames. It contains all of the text data, annotations, supplemental resources and related software tools for the Oromo language that were used in the DARPA LORELEI / LoReHLT 2017 Evaluation.

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.

The evaluation protocol was based on a scenario in which an unforeseen event triggered a need for humanitarian and logistical support in a region where the incident language had received little or no attention in natural language processing (NLP) research. Evaluation participants provided NLP solutions, including information extraction and machine translation, based on limited resources and with very little time for development.

Data

Oromo is a Cushitic language spoken in Ethiopia, Kenya, Somalia and Egypt, and it is the third largest language in Africa. Data was collected in the following genres: news, social network, weblog, newsgroup, discussion forum, and reference material.

Entity detection and linking annotation identified entities to be detected by systems for scoring purposes. Situation frame analysis was designed to extract basic information about needs and relevant issues for planning a disaster response effort.

Also included in this release are lexical and grammatical resources as well as three tools: two to recreate original source data from the processed XML material and the other to condition text data users download from Twitter.

Monolingual, parallel and comparable text are presented in XML with associated dtds. Entity Detection and Linking and Situation Frame annotation data is presented as tab delimited files. All text is UTF-8 encoded.

The knowledge base for entity linking annotation in 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

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Updates

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