The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off

There is a vast amount of unstructured Arabic information on the Web, this data is always organized in semi-structured text and cannot be used directly. This research proposes a semi-supervised technique that extracts binary relations between two Arabic named entities from the Web. Several works have been performed for relation extraction from Latin texts and as far as we know, there isn't any work for Arabic text using a semi-supervised technique. 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 are 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.


Relation Extraction, Information Extraction, Pattern Extraction, Semi-Supervised, Arabic language and Web Mining.
Font Size