LORELEI Hungarian Representative Language Pack consists of Hungarian monolingual text, Hungarian-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. Hungarian is spoken mainly in Hungary, where it is an official language, as well as in neighboring countries such as Romania, Slovakia, and Serbia. 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: 686 million words of Hungarian monolingual text, 165,000 words of which were translated into English 2.3 million words of found Hungarian-English parallel text 87,000 Hungarian words translated from English data Approximately 72,500 words were annotated for named entities; about 25,000 words were annotated for full entity (including nominals and pronouns), entity linking and situation frames; over 17,000 words have simple semantic annotation; and close to 10,000 words were annotated for noun phrase chunking. 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).

Creator(s)

Jennifer Tracey

Stephanie Strassel

David Graff

Jonathan Wright

Song Chen

Neville Ryant

Seth Kulick

Kira Griffitt

Dana Delgado

Michael Arrigo

Distributor(s)

Linguistic Data Consortium

Right Holder(s)

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