نتایج جستجو برای: self mention
تعداد نتایج: 541908 فیلتر نتایج به سال:
Research Article (RA) genre has been a significant area of research in academic writing over past decades. However, authors’ identity in RAs has not received much attention, especially in soft sciences like applied linguistics. This paper reports a corpus analysis of Iranian writers’ authorial presence markers in RAs in the field of applied linguistics. The corpus comprised 30 RAs (200,000 word...
In this paper we propose the self-healing feature for group key distribution through Subset Difference (SD) method proposed by D. Naor et al. The subset difference method is one of the efficient proposals for group key distribution, however, recently a polynomial based solution for key distribution was proposed by D. Liu et al., which has the similar message size but also provides self-healing ...
A Survey of Self-Organizing Data-Structures Susanne Albers and Jeffery Westbrook Self-organizing data structures modify their structure while processing a sequence of operations. The data structure is allowed to be in an arbitrary state, i.e. no global constraint has to be satisfied, but during each operation a simple restructuring rule is applied. The purpose of this restructuring is to guaran...
PURPOSE To examine viewers' comment responses to nonsuicidal self-injury (NSSI) YouTube videos to determine the potential risks (e.g., NSSI continuation) and benefits (e.g., recovery-oriented social support) of the videos. METHODS Viewers' comments from the 100 most-viewed NSSI videos on YouTube were examined using two coding rubrics, one for the global nature of comments and one for recovery...
Our submission was a reduced version of the system described in Haghighi and Klein (2010), with extensions to improve mention detection to suit the OntoNotes annotation scheme. Including exact matching mention detection in this shared task added a new and challenging dimension to the problem, particularly for our system, which previously used a very permissive detection method. We improved this...
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