DOI: 10.35111/5b6x-2e75
title: LORELEI Ilocano Incident Language Pack

description: <h3>Introduction</h3><br>
<p>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 <a href="https://www.nist.gov/itl/iad/mig/lorehlt-evaluations">DARPA LORELEI / LoReHLT 2019 Evaluation</a>.</p><br>
<p>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.</p><br>
<p>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.</p><br>
<h3>Data</h3><br>
<p>Ilocano is spoken in the Philippines. Data was collected in the following genres: news, social network, weblog, newsgroup, discussion forum, and reference material.</p><br>
<p>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.</p><br>
<p>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.</p><br>
<p>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.</p><br>
<p>The knowledge base for entity linking annotation for this corpus and all LORELEI Representative Language and Incident Language Packs is available separately as <a href="../../../LDC2020T10">LORELEI Entity Detection and Linking Knowledge Base (LDC2020T10)</a>.</p><br>
<h3>Acknowledgement</h3><br>
<p>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.</p><br>
<h3>Updates</h3><br>
<p>No updates at this time.</p></br>
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