2011 | OriginalPaper | Chapter
ZamAn and Raqm: Extracting Temporal and Numerical Expressions in Arabic
Authors : Iman Saleh, Lamia Tounsi, Josef van Genabith
Published in: Information Retrieval Technology
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In this paper we investigate automatic identification of Arabic temporal and numerical expressions. The objectives of this paper are 1) to describe
ZamAn
, a machine learning method we have developed to label Arabic temporals, processing the functional dashtag -TMP used in the Arabic treebank to mark a temporal modifier which represents a reference to a point in time or a span of time, and 2) to present
Raqm
, a machine learning method applied to identify different forms of numerical expressions in order to normalise them into digits.
We present a series of experiments evaluating how well
ZamAn
(resp.
Raqm
) copes with the enriched Arabic data achieving state-of-the-art results of F1-measure of 88.5% (resp. 96%) for bracketing and 73.1% (resp. 94.4%) for detection.