NDLI: Statistical transliteration for english-arabic cross language information retrieval

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Statistical transliteration for english-arabic cross language information retrieval

Content Provider	ACM Digital Library
Author	Larkey, Leah S. AbdulJaleel, Nasreen
Abstract	Out of vocabulary (OOV) words are problematic for cross language information retrieval. 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. In the present study, we present a simple statistical technique to train an English to Arabic transliteration model from pairs of names. We call this a selected n-gram model because a two-stage training procedure first learns which n-gram segments should be added to the unigram inventory for the source language, and then a second stage learns the translation model over this inventory. This technique requires no heuristics or linguistic knowledge of either language. We evaluate the statistically-trained model and a simpler hand-crafted model on a test set of named entities from the Arabic AFP corpus and demonstrate that they perform better than two online translation sources. We also explore the effectiveness of these systems on the TREC 2002 cross language IR task. We find that transliteration either of OOV named entities or of all OOV words is an effective approach for cross language IR.
Starting Page	139
Ending Page	146
Page Count	8
File Format	PDF
ISBN	1581137230
DOI	10.1145/956863.956890
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Named entities Statistical transliteration Out of vocabulary words Cross language information retrieval
Content Type	Text
Resource Type	Article

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