|Author(s):||Amal Alsaif, Tasniem Alyahya, Madawi Alotibi, Huda Almuzaini, Abeer Alqahtani|
|LDC Catalog No.:||LDC2022T02|
|Release Date:||March 15, 2022|
|DCMI Type(s):||Software, Text|
|Application(s):||discourse analysis, entity extraction, language identification|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2022T02 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Alsaif, Amal, et al. AttImam LDC2022T02. Web Download. Philadelphia: Linguistic Data Consortium, 2022.|
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.
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.
Please view this sample (TXT).
None at this time.