In this newsletter: LDC membership discounts expire March 1 30th Anniversary Highlight: Arabic Treebank
New publications: 2019 NIST Speaker Recognition Evaluation Test Set - Audio-Visual<https://catalog.ldc.upenn.edu/LDC2023V01> LORELEI Tagalog Representative Language Pack<https://catalog.ldc.upenn.edu/LDC2023T02> ________________________________ LDC membership discounts expire March 1 Time is running out to save on 2023 membership fees. Renew your LDC membership, rejoin the Consortium, or become a new member by March 1 to receive a discount of up to 10%. For more information on membership benefits and options, visit Join LDC<https://www.ldc.upenn.edu/members/join-ldc>. 30th Anniversary Highlight: Arabic Treebank The Penn/LDC Arabic Treebank (ATB) project began in 2001 with support from the DARPA TIDES program and later, the DARPA GALE and BOLT programs. The original focus was on Modern Standard Arabic (MSA), not natively spoken and not homogenously acquired across its writing and reading community. In addition to the expected issues associated with complex data annotation, LDC encountered several challenges unique to a highly inflected language with a rich history of traditional grammar. LDC relied on traditional Arabic grammar, as well as established and modern grammatical theories of MSA -- in combination with the Penn Treebank approach to syntactic annotation -- to design an annotation system for Arabic. (Maamouri, et al., 2004<https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/nemlar2004-penn-arabic-treebank.pdf>). LDC was innovative with respect to traditional grammar when necessary and when other syntactic approaches were found to account for the data. LDC also developed a wide-coverage MSA morphological analyzer, LDC Standard Arabic Morphological Analyzer (SAMA) Version 3.1 (LDC2010L01<https://catalog.ldc.upenn.edu/LDC2010L01>), which greatly benefited ATB development. Revisions to the annotation guidelines during the DARPA GALE program (principally related to tokenization and syntactic annotation) improved inter-annotator agreement and parsing scores. ATB corpora were annotated for morphology, part-of-speech, gloss, and syntactic structure. Data sets based on MSA newswire developed under the revised annotation guidelines include Arabic Treebank: Part 1 v 4.1 (LDC2010T13<https://catalog.ldc.upenn.edu/LDC2010T13>), Arabic Treebank: Part 2 v 3.1 (LDC0211T09<https://catalog.ldc.upenn.edu/LDC2011T09>), and Arabic Treebank: Part 3 v 3.2 (LDC2010T08<https://catalog.ldc.upenn.edu/LDC2010T08>). Other genres are represented in Arabic Treebank - Broadcast News v 1.0 (LDC2012T07<https://catalog.ldc.upenn.edu/LDC2012T07>) and Arabic Treebank - Weblog (LDC2016T02<https://catalog.ldc.upenn.edu/LDC2016T02>). LDC's later work on Egyptian Arabic treebanks in the DARPA BOLT program benefited from the strides in its MSA treebank annotation pipeline. As for the challenges presented by informal, dialectal material, collaborator Columbia University provided a normalized Arabic orthography to account for instances of Romanized script (Arabizi) in the data and developed a morphological analyzer (CALIMA) in parallel, working in a tight feedback loop with LDC's annotation team. SAMA and CALIMA were synchronized in the Egyptian Arabic treebanks, the former used for MSA tokens and the latter used for Egyptian Arabic tokens. Resulting corpora include BOLT Egyptian Arabic Treebank - Discussion Forum (LDC2018T23<https://catalog.ldc.upenn.edu/LDC2018T23>), Conversational Telephone Speech (LDC2021T12<https://catalog.ldc.upenn.edu/LDC2021T12>), and SMS/Chat (LDC2021T17<https://catalog.ldc.upenn.edu/LDC2021T17>). ATB corpora and its related releases are available for licensing to LDC members and nonmembers. For more information about licensing LDC data, visit Obtaining Data<https://www.ldc.upenn.edu/language-resources/data/obtaining> ________________________________ New publications: 2019 NIST Speaker Recognition Evaluation Test Set - Audio-Visual<https://catalog.ldc.upenn.edu/LDC2023V01> contains approximately 64 hours of English audio-visual data for development and test, answer keys, enrollment, trial files, and documentation from the NIST-sponsored 2019 Speaker Recognition Evaluation (SRE)<https://www.nist.gov/itl/iad/mig/nist-2019-speaker-recognition-evaluation>. The 2019 evaluation task was speaker detection, that is, to determine whether a specified target speaker was speaking during a segment of speech. The evaluation was conducted in two parts: (1) a leaderboard-style challenge based on conversational telephone speech and (2) a separate evaluation using audio-visual data. This release relates to the audio-visual evaluation. The source audio-visual data was collected by LDC for the VAST (Video Annotation for Speech Technology) project. That collection focused on amateur video recordings from various online media hosting services. The recordings vary in duration from 17.5 seconds to 13 minutes; most have two audio channels (stereo), but some are monophonic (one channel). 2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. * LORELEI Tagalog Representative Language Pack<https://catalog.ldc.upenn.edu/LDC2023T02> was developed by LDC and is comprised of approximately 4.8 million words of Tagalog monolingual text, 341,000 words of found Tagalog-English parallel text, and 124,000 Tagalog words translated from English data. Approximately 78,000 words were annotated for named entities and over 26,000 words were annotated for entity discovery and linking and situation frames (identifying entities, needs and issues). Data was collected from discussion forum, news, reference, social network, and weblogs. 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. Representative languages were selected to provide broad typological coverage. The knowledge base for entity linking annotation is available separately as LORELEI Entity Detection and Linking Knowledge Base (LDC2020T10)<https://catalog.ldc.upenn.edu/LDC2020T10>. 2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. To unsubscribe from this newsletter, log in to your LDC account<https://catalog.ldc.upenn.edu/login> and uncheck the box next to "Receive Newsletter" under Account Options or contact LDC for assistance. Membership Coordinator Linguistic Data Consortium<ldc.upenn.edu> University of Pennsylvania T: +1-215-573-1275 E: [email protected]<mailto:[email protected]> M: 3600 Market St. Suite 810 Philadelphia, PA 19104
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