In this paper we investigate automatic identification of Arabic temporal and numerical expressions. The objectives of this paper are 1) to describe
, 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
, 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
) 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.