Mixed-Language Arabic- English Information Retrieval

Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries

Machine transliteration of proper names between English and Persian

Machine transliteration is the process of automatically transforming a word from a source language to a target language while preserving pronunciation. The transliterated words in the target language are called out-of-dictionary, or sometimes out-of-vocabulary, meaning that they have been borrowed from other languages with a change of script. When a whole text is being translated, for example, then proper nouns and technical terms are subject to transliteration. Machine translation, and other applications which make use of this technology, such as cross-lingual information retrieval and cross-language question answering, deal with the problem of transliteration. Since proper nouns and technical terms - which need phonetical translation - are part of most text documents, transliteration is an important problem to study. We explore the problem of English to Persian and Persian to English transliteration using methods that work based on the grapheme of the source word. One major problem in handling Persian text is its lack of written short vowels. When transliterating Persian words to English, we need to develop a method of inserting vowels to make them pronounceable. Many different approaches using n-grams are explored and compared in this thesis, and we propose language-specific transliteration methods that improved transliteration accuracy. Our novel approaches use consonant-vowel sequences, and show significant improvements over baseline systems. We also develop a new alignment algorithm, and examine novel techniques to combine systems; approaches which improve the effectiveness of the systems. We also investigate the properties of bilingual corpora that affect transliteration accuracy. Our experiments suggest that the origin of the source words has a strong effect on the performance of transliteration systems. From the careful analysis of the corpus construction process, we conclude that at least five human transliterators are needed to construct a representative bilingual corpus that is used for the training and testing of transliteration systems