Similarity-based model for transliteration
A significant proportion of out of vocabulary (OOV) words are named entities and technical terms. Typical analyses find around 50% of OOV words to be named entities. Yet these can be the most important words in the queries. For example, in the list of queries for TREC 2001 cross-language track, all 25 queries contained proper names. Cross-language retrieval performance (average precision) reduced more than 50% when named entities in the queries were not translated. One way to deal with OOV words when the two languages have different alphabets is to transliterate the unknown words, that is, to render them in the orthography of the second language. Transliteration is the process of formulating a representation of words in one language using the alphabet of another language. In the present study, we present different approaches for transliteration of proper noun pair’s extraction from parallel corpora based on different similarity measures between the English and the romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the presented new approaches using two different English–Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F-Measure.
KeywordsProper Noun Sentence Pair Parallel Corpus Short Vowel Arabic Word
This research has been partially supported by the Japan Society for the Promotion of Science (JSPS), Grant No. 07077, and the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (B), 19300029.
- 1.Al-Onaizan Y, Knight K (2002) Translating named entities using monolingual and bilingual resources. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp 400–408Google Scholar
- 2.Stalls B, Knight K (1998) Translating names and technical terms in Arabic text. In: Proceedings of the COLING/ACL Workshop on Computational Approaches to Semitic LanguagesGoogle Scholar
- 3.Chen HH, Huang SJ, Ding YW, Tsai SC (1998) Proper name translation in cross-language information retrieval. In: Proceedings of 17th COLING and 36th ACL, pp 232–236Google Scholar
- 4.Knight K, Graehl J (1998) Machine transliteration. Computational Linguistics 24(4):599– 612Google Scholar
- 7.Fung P (1995) A pattern matching method for finding noun and proper noun translations from noisy parallel corpora. CoRR cmp-lg/9505016Google Scholar
- 8.Fung P, Yee L (1998) An IR approach for translating new words from nonparallel, comparable texts. COLING-ACL, pp 414-420Google Scholar
- 9.Fung P (1998) A statistical view on bilingual lexicon extraction: from parallel corpora to non-parallel corpora. AMTA (1998), pp 1-17Google Scholar
- 10.McEnery AM, Oakes MP (1996) Sentence and word alignment in the crater project. In: Thomas J, Short M (eds) Using Corpora for Language Research, Longman, London, pp 211–231Google Scholar