LORELEI Farsi Representative Language Pack

Full Official Name: LORELEI Farsi Representative Language Pack
Submission date: Jan. 16, 2024, 11:18 p.m.

LORELEI Farsi Representative Language Pack consists of Farsi monolingual text, Farsi-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. Farsi is spoken mainly in Iran and Afghanistan; it is the official language in both countries. 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: 250 million words of Farsi monolingual text, over 391,000 of which were translated into English 751,000 words of found Farsi-English parallel text 120,000 Farsi words translated from English data Approximately 75,000 words are annotated for named entities, and over 22,000 words were annotated for full entity including nominals and pronouns, simple semantic annotation, situation frame annotation, and entity linking. 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).

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