LORELEI Ilocano Incident Language Pack
Bibliographic record
Abstract
Introduction LORELEI Ilocano Incident Language Pack (LDC2025T16) was developed by the Linguistic Data Consortium (LDC) and consists of approximately 8.9 million words of Ilocano monolingual text, 3.3 million words of English monolingual text, 3.2 million words of parallel Ilocano-English text, and 3 million 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 Ilocano language that were used in the DARPA LORELEI / LoReHLT 2019 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 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 some unforeseen event triggered a need for humanitarian and logistical support in a region where the predominant language was one that 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 Ilocano is spoken in the Philippines. Data was collected in the following genres: news, social network, weblog, newsgroup, discussion forum, and reference material. Entity discovery 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 X/Twitter. Monolingual, parallel and comparable text are presented in XML with associated dtds. Entity discovery and linking annotation and situation frame annotation are 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. Updates No updates at this time. Portions © 2019 agenparl.eu, © 2018 American Radio Relay League, © 2016-2019 Amianan Balita Ngayon, © 2019 BBC, © 2018-2019 Bombo Radyo Philippines, © 2018-2019 Cable News Network. Turner Broadcasting System, Inc., © 2018 CBC/Radio-Canada, © 2019 Condé Nast, © 2018 Cordillera Peoples Alliance, © 2018 Crisis Group, © 2019 Dow Jones & Company, Inc., © 2018 Edipresse Media Asia Limited, ©2018 Express Newspapers, © 2018 Galadari Printing and Publishing LLC, © 2018 GardaWorld, © 2019 Got Questions Ministries, © 2018 Guardian News and Media Limited or its affiliated companies, © 2017-2019 Ilocos Sentinel - The Forerunner in Weekly News, © 2019 ISA, International Sociological Association, © 2018 Jpost Inc., © 2018 Los Angeles Times, © 2018 mb.com.ph, © 2018 Microsoft, © 2018 Mindanews, © 2019 National Geographic Society|National Geographic Partners, LLC, © 2018 News Pty Limited, © 2005-2019 Northern Dispatch, © 2018 npr, © 2018 Pacific Media Centre, © 2019 POLITICO LLC, © 2018 primer.com.ph, © 2018-2019 Remate News Central, © 2017-2019 RMN Networks, © 2018 Rogers Media, © 2018 South China Morning Post Publishers Ltd., © 2018 Special Broadcasting Service Corporation, © 2018-2019 SunStar Publishing Inc., © 2016-2019 Tawid News Magazine, © 2019 Telegraph Media Group Limited, © 2018 The Irish Times, © 2019 The New York Times Company, © 2018 The Social Justice Foundation, © 2018 The Times of Israel, © 2018 Toronto Star Newspapers Ltd., © 2019 United Methodist Communications, © 2018 United Nations Office for the Coordination of Humanitarian Affairs, © 2018 United Press International, Inc., © 2019 Vox Media, Inc., © 2018 Wells Media Group, Inc., © 2018 Winslow Record, © 2019, 2025 Trustees of the University of Pennsylvania
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.086 |
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".