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In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the...
A lot of natural language processing (NLP) applications require the computation of similarities between pairs of syntactic or semantic trees. Tree edit distance (TED), in this context, is considered to be one of the most effective techniques. However, its main drawback is that it deals with single node operations only. We therefore extended TED to deal with subtree transformation operations as well...
A number of trainable dependency parsers have been presented in the literature. These parsers require tagged input: this may potentially cause a problem, because taggers are not in general 100% accurate, and any errors in tagging are likely to lead to errors in the output of the parsers. The current paper investigates the relationship between tagging errors and parsing errors. The investigation is...
In this paper, we present BASRAH, a system that automatically identifies the meter of Arabic verse, which is an operation that requires a certain level of human expertise. BASRAH uses the numerical prosody method, which depends on verse coding that is derived from the general concept of al-Khalil's feet through using the two primary units (cord=2 and peg=3). BASRAH has proved to be an efficient tool...
The aim of the work reported here is to investigate the effectiveness of dependency parsing for the analysis of Arabic. Arabic has a number of characteristics, described below, which make parsing it particularly challenging. The results of our investigations suggest that dependency parsing can produce reasonably accurate results. We show in particular that combining the output of two different parsers...
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