Extracting Arabic Relations from the Web
This addresses the challenge of extracting structured information from Arabic web text for applications like knowledge base construction, though it is incremental as it builds on existing relation extraction methods.
The researchers tackled the problem of extracting Arabic relations from web search summaries using a small set of user-provided instances to generate patterns, achieving precision up to 0.75 and recall up to 0.83 across four experiments.
The goal of this research is to extract a large list or table from named entities and relations in a specific domain. A small set of a handful of instance relations is required as input from the user. The system exploits summaries from Google search engine as a source text. These instances are used to extract patterns. The output is a set of new entities and their relations. The results from four experiments show that precision and recall varies according to relation type. Precision ranges from 0.61 to 0.75 while recall ranges from 0.71 to 0.83. The best result is obtained for (player, club) relationship, 0.72 and 0.83 for precision and recall respectively.