oai:www.ldc.upenn.edu:LDC2022T02
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OLAC Record oai:www.ldc.upenn.edu:LDC2022T02 |
| Metadata | ||
| Title: | AttImam | |
| Access Rights: | Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining | |
| Bibliographic Citation: | Alsaif, Amal, et al. AttImam LDC2022T02. Web Download. Philadelphia: Linguistic Data Consortium, 2022 | |
| Contributor: | Alsaif, Amal | |
| Alyahya, Tasniem | ||
| Alotibi, Madawi | ||
| Almuzaini, Huda | ||
| Alqahtani, Abeer | ||
| Date (W3CDTF): | 2022 | |
| Date Issued (W3CDTF): | 2022-03-15 | |
| Description: | *Introduction* AttImam was developed by Al-Imam Mohammad Ibn Saud Islamic University and consists of approximately 2,000 attribution relations applied to Arabic newswire text from Arabic Treebank: Part 1 v 4.1 (LDC2010T13). Attribution refers to the process of reporting or assigning an utterance to the correct speaker. *Data* The source Arabic newswire was collected by the Linguistic Data Consortium from Agence France Presse articles published in 2000. Annotation was performed by two native Arabic speakers. Each file has the following four elements: * Cue: the lexical anchor that connects the source with the content. * Source: the entity or the agent that owns the content. * Content: the basic element expressing the claim or the reported news. * General Features: these can include such features as attribution style (direct or indirect), determinacy (factual or non-factual), and purpose (e.g., assertion, expression). The corpus contains 532 files in UTF-8 encoded plain text. Also included are annotation guidelines and the ESNAD (Extracting Sentence Attribution in Arabic Discourse) annotation tool. *Samples* Please view this sample (TXT). *Updates* None at this time. | |
| Extent: | Corpus size: 8837 KB | |
| Identifier: | LDC2022T02 | |
| https://catalog.ldc.upenn.edu/LDC2022T02 | ||
| ISBN: 1-58563-987-7 | ||
| ISLRN: 597-668-301-473-2 | ||
| DOI: 10.35111/66b0-j250 | ||
| Language: | Standard Arabic | |
| Language (ISO639): | arb | |
| License: | LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf | |
| Medium: | Distribution: Web Download | |
| Publisher: | Linguistic Data Consortium | |
| Publisher (URI): | https://www.ldc.upenn.edu | |
| Relation (URI): | https://catalog.ldc.upenn.edu/docs/LDC2022T02 | |
| Rights Holder: | Portions © 2000 Agence France Presse, © 2022 Amal S. Alsaif, © 2003, 2005, 2010, 2022 Trustees of the University of Pennsylvania | |
| Type (DCMI): | Software | |
| Text | ||
| Type (OLAC): | primary_text | |
OLAC Info |
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| Archive: | The LDC Corpus Catalog | |
| Description: | http://www.language-archives.org/archive/www.ldc.upenn.edu | |
| GetRecord: | OAI-PMH request for OLAC format | |
| GetRecord: | Pre-generated XML file | |
OAI Info |
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| OaiIdentifier: | oai:www.ldc.upenn.edu:LDC2022T02 | |
| DateStamp: | 2023-01-01 | |
| GetRecord: | OAI-PMH request for simple DC format | |
Search Info | ||
| Citation: | Alsaif, Amal; Alyahya, Tasniem; Alotibi, Madawi; Almuzaini, Huda; Alqahtani, Abeer. 2022. Linguistic Data Consortium. | |
| Terms: | area_Asia country_SA dcmi_Software dcmi_Text iso639_arb olac_primary_text | |