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Revitalizing Endangered Languages

Edited by Justyna Olko & Julia Sallabank

Revitalizing Endangered Languages "This guidebook provides ideas and strategies, as well as some background, to help with the effective revitalization of endangered languages. It covers a broad scope of themes including effective planning, benefits, wellbeing, economic aspects, attitudes and ideologies."

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Dissertation Information

Title: Towards the Development of an Automatic Diacritizer for the Persian Orthography based on the Xerox Finite State Transducer Add Dissertation
Author: Peyman Nojoumian Update Dissertation
Email: click here to access email
Institution: University of Ottawa, Department of Linguistics
Completed in: 2011
Linguistic Subfield(s): Computational Linguistics;
Director(s): Paul Hirschbühler
Diana Inkpen

Abstract: Due to the lack of short vowels or diacritics in Persian orthography, many
Natural Language Processing applications for this language, including
information retrieval, machine translation, text-to-speech, and automatic
speech recognition systems need to disambiguate the input first, in order
to be able to do further processing. In machine translation, for example,
the whole text should be correctly diacritized first so that the correct
words, parts of speech and meanings are matched and retrieved from the
lexicon. This is primarily because of Persian’s ambiguous orthography. In
fact, the core engine of any Persian language processor should utilize a
diacritizer and a lexical disambiguator. This dissertation describes the
design and implementation of an automatic diacritizer for Persian based on
the state-of-the-art Finite State Transducer technology developed at Xerox
by Beesley & Karttunen (2003). The result of morphological analysis and
generation on a test corpus is shown, including the insertion of
diacritics. This study will also look at issues that are raised by
phonological and semantic ambiguities as a result of short vowels in
Persian being absent in the writing system. It suggests a hybrid model
(rule-based & inductive) that is inspired by psycholinguistic experiments
on the human mental lexicon for the disambiguation of heterophonic
homographs in Persian using frequency and collocation information. A
syntactic parser can be developed based on the proposed model to discover
Ezafe (the linking short vowel /e/ within a noun phrase) or disambiguate
homographs, but its implementation is left for future work.