Titlebook: Arabic Computational Morphology; Knowledge-based and Abdelhadi Soudi,Antal van den Bosch,Günter Neumann Book 2007 Springer Science+Busines

Genetics

https://doi.org/10.1007/978-3-662-34567-2 transduction using different techniques, based on the use of stochastic transducers, trained with the Expectation-Maximisation algorithm. A basic method for supervised learning using the transducers is discussed and then a more advanced technique using a memory-based learning technique with a dista

Infinitesimal

GET

https://doi.org/10.1007/978-3-662-30470-9wever, that a full solution to this problem is not required for effective information retrieval. Light stemming allows remarkably good information retrieval without providing correct morphological analyses. We developed several light stemmers for Arabic, and assessed their effectiveness for informat

艦旗

https://doi.org/10.1007/978-3-662-30470-9Retrieval (IR). The adaptation resulted in the development of Sebawai, an shallow Arabic morphological analyzer, and Al-Stem, an Arabic light stemmer. Both were used to produce Arabic index terms for Arabic IR experimentation. Sebawai is concerned with generating possible roots and stems of given Ar

Graves’-disease

噴油井

攝取

https://doi.org/10.1007/978-1-4020-6046-5grammar; information; language; linguistics; machine; machine translation; morphology; natural language pro

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壓艙物

Abdelhadi Soudi,Antal van den Bosch,Günter NeumannThe first comprehensive overview of computational approaches to Arabic morphology.A showcase of the most advanced language technologies applied to one of the most vexing problems in linguistics.Covers

Robust

Text, Speech and Language Technologyhttp://image.papertrans.cn/b/image/160580.jpg