LORELEI Akan Representative Language Pack
Domaine:
natural language processingType de record:
datasetsoftware
Créateur:
TraStrGraWri
Éditeur:
Lin
Hôte:
Introduction
LORELEI Akan Representative Language Pack consists of Akan monolingual text, Akan-English parallel text, annotations, supplemental resources and related software tools developed by LDC 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
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 are annotated for named entities, full entity including nominals and pronouns, entity linking, simple semantic annotation, and situation frame annotation, and approximately 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). 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:
Akan LTF XML Akan PSM XML English PSM XML English LTF XML Sentence Alignment (XML) Simple Name Entitey Annotation (XML) Full Name Entity Annotation (XML) Semantic Annotatation (XML)
Updates
None at this time. Copyright Portions © 2002-2007, 2009-2010 Agence France Presse, © 2000 American Broadcasting Company, © 2000 Cable News Network LP, LLLP, © 2008 Central News Agency (Taiwan), © 1989 Dow Jones & Company, Inc., © 2008 Five Colleges, Incorporated, © 2005 Los Angeles Times - Washington Post News Service, Inc., © 2000 National Broadcasting Company, Inc., © 1999, 2005, 2006, 2010 New York Times, © 2017 NY State of Health, © 2000 Public Radio International, © 2003, 2005-2008, 2010 The Associated Press, © 2017 Toronto Community Housing Corporation, © 2011-2017 Watch Tower Bible and Tract Society of Pennsylvania, © 2003, 2005-2008 Xinhua News Agency, © 2021 Trustees of the University of Pennsylvania
LORELEI Akan Representative Language Pack consists of Akan monolingual text, Akan-English parallel text, annotations, supplemental resources and related software tools developed by LDC 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
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 are annotated for named entities, full entity including nominals and pronouns, entity linking, simple semantic annotation, and situation frame annotation, and approximately 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). 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:
Akan LTF XML Akan PSM XML English PSM XML English LTF XML Sentence Alignment (XML) Simple Name Entitey Annotation (XML) Full Name Entity Annotation (XML) Semantic Annotatation (XML)
Updates
None at this time. Copyright Portions © 2002-2007, 2009-2010 Agence France Presse, © 2000 American Broadcasting Company, © 2000 Cable News Network LP, LLLP, © 2008 Central News Agency (Taiwan), © 1989 Dow Jones & Company, Inc., © 2008 Five Colleges, Incorporated, © 2005 Los Angeles Times - Washington Post News Service, Inc., © 2000 National Broadcasting Company, Inc., © 1999, 2005, 2006, 2010 New York Times, © 2017 NY State of Health, © 2000 Public Radio International, © 2003, 2005-2008, 2010 The Associated Press, © 2017 Toronto Community Housing Corporation, © 2011-2017 Watch Tower Bible and Tract Society of Pennsylvania, © 2003, 2005-2008 Xinhua News Agency, © 2021 Trustees of the University of Pennsylvania