Bootstrapping a Stochastic Transducer for Arabic-English Transliteration Extraction

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 864–871, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Bootstrapping a Stochastic Transducer for Arabic-English Transliteration Extraction Tarek Sherif and Grzegorz Kondrak Department of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 {tarek,kondrak}@cs.ualberta.ca Abstract We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The trans- ducer learns its similarity metric from the data in the bitext, and thus can func- tion directly on strings written in different writing scripts without any additional lan- guage knowledge. We show that this boot- strapped transducer performs as well or bet- ter than a model designed specifically to de- tect Arabic-English transliterations. 1 Introduction Transliterations are words that are converted from one writing script to another on the basis of their pro- nunciation, rather than being translated on the basis of their meaning. Transliterations include named en- tities (e.g. � � � � � � @ � � � � g . /Jane Austen) and lexical loans (e.g. � � �K � � Q � � � � � � K/television). An algorithm to detect transliterations automati- cally in a bitext can be an effective tool for many tasks. Models of machine transliteration such as those presented in (Al-Onaizan and Knight, 2002) or (AbdulJaleel and Larkey, 2003) require a large set of sample transliterations to use for training. If such a training set is unavailable for a particular language pair, a detection algorithm would lead to a signif- icant gain in time over attempting to build the set manually. Algorithms for cross-language informa- tion retrieval often encounter the problem of out-of- vocabulary words, or words not present in the algo- rithm’s lexicon. Often, a significant proportion of these words are named entities and thus are candi- dates for transliteration. A transliteration detection algorithm could be used to map named entities in a query to potential transliterations in the target lan- guage text. The main challenge in transliteration detection lies in the fact that transliteration is a lossy process. In other words, information can be lost about the original word when it is transliterated. This can oc- cur because of phonetic gaps in one language or the other. For example, the English [p] sound does not exist in Arabic, and the Arabic [Q] sound (made by the letter �) does not exist in English. Thus, Paul is transliterated as � �K . [bul], and � � � � [Qali] is translit- erated as Ali. Another form of loss occurs when the relationship between the orthographic and phonetic representations of a word are unclear. For example, the [k] sound will always be written with the letter in Arabic, but in English it can be written as c, k ch,� ck, cc or kk (not to mention being one of the sounds produced by x). Finally, letters may be deleted in one language or the other. In Arabic, …

Content truncated to 500 words