Abstract
Arabic parsing is an important task in several NLP applications. Indeed to obtain a robust, efficient and extensible parser treating several phenomena, several issues (i.e., ambiguity and embedded structures) must be resolved. In this context, we will build an Arabic parser based on a deep linguistic study done with a new vision allowing the problem division and on a transducer cascade implemented in the NooJ linguistic platform. This parser is accomplished through our designed dictionaries, morphological grammars and transducers recognizing different sentence forms. The constructed parser is applied to two test corpora containing more than 5900 sentences with different structures. The parser outputs are XML annotated sentences. To evaluate the obtained results, we calculated the measure values of the precision, the recall and the f-measure, and compare them with those obtained by recursive transducer parser. The calculated measure values show that these results are encouraging.
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Ghezaiel Hammouda, N., Torjmen, R., Haddar, K. (2018). Transducer Cascade to Parse Arabic Corpora. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_22
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DOI: https://doi.org/10.1007/978-3-319-91947-8_22
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