LORELEI Akan Representative Language Pack
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.302 | 0.275 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".