LORELEI Akan Representative Language Pack

Item Name: LORELEI Akan 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.: LDC2021T02
ISBN: 1-58563-952-4
ISLRN: 301-247-285-991-8
DOI: https://doi.org/10.35111/srvm-p675
Release Date: January 15, 2021
Member Year(s): 2021
DCMI Type(s): Software, Text
Data Source(s): discussion forum, newswire, religious texts, web collection, weblogs
Project(s): LORELEI
Application(s): cross-language transfer, entity extraction, information extraction, machine translation
Language(s): Akan, English
Language ID(s): aka, eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2021T02 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Tracey, Jennifer, et al. LORELEI Akan Representative Language Pack LDC2021T02. Web Download. Philadelphia: Linguistic Data Consortium, 2021.
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LORELEI Akan Representative Language Pack consists of Akan monolingual text, Akan-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.


Akan is spoken mainly in Ghana and Ivory Coast. 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 3.3 million words of Akan monolingual text, all of which were translated into English
  • 115,000 Akan words translated from English data

Approximately 2,300 words were annotated for named entities, full entity including nominals and pronouns, entity linking, simple semantic annotation, and situation frame annotation (identifying entities, needs and issues). Around 2,000 words have morphological segmentation annotation.

Lexical resources and software tools are also included in this release. The tools recreate original source data from the processed XML material, condition text data users download from Twitter, apply sentence segmentation to raw text, and support named entity tagging.

Monolingual and parallel text are presented in XML with associated dtds. Annotation data is presented as tab delimited files or XML. 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).


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.


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