Statistical parsing by machine learning from a Classical Arabic Treebank
Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i'raÌb (Ű§ÙŰčŰșŰ§Ű© ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis,
Dukes, Kais
Statistical parsing by machine learning from a Classical Arabic Treebank. Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i'raÌb (Ű§ÙŰčŰșŰ§Ű© ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis,
statistical parsing machine_learn classical arabic statistical parse english enjoy decade successful result however adapt model languages meet difficulty previous comparative work show modern arabic one_difficult languages parse due rich morphology free_word order classical_arabic ancient form arabic understudied computational_linguistics relative worldwide reach language quran base publication make_significant contribution knowledge relate annotate parse classical_arabic classical_arabic study depth grammarian use traditional grammar know using grammar develop representation parse challenging describe syntax use hybrid phrase_structure dependency_relation work aim_advance state_of_the_art hybrid parse introduce formal representation annotation resource machine learning main contribution first classical_arabic first statistical dependency_based parser language ellipsis