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Transliteration Equivalence Using Canonical Correlation Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)

Abstract

We address the problem of Transliteration Equivalence, i.e. determining whether a pair of words in two different languages (e.g. Auden, ऑडेन) are name transliterations or not. This problem is at the heart of Mining Name Transliterations (MINT) from various sources of multilingual text data including parallel, comparable, and non-comparable corpora and multilingual news streams. MINT is useful in several cross-language tasks including Cross-Language Information Retrieval (CLIR), Machine Translation (MT), and Cross-Language Named Entity Retrieval. We propose a novel approach to Transliteration Equivalence using language-neutral representations of names. The key idea is to consider name transliterations in two languages as two views of the same semantic object and compute a low-dimensional common feature space using Canonical Correlation Analysis (CCA). Similarity of the names in the common feature space forms the basis for classifying a pair of names as transliterations. We show that our approach outperforms state-of-the-art baselines in the CLIR task for Hindi-English (3 collections) and Tamil-English (2 collections).

Keywords

Information Retrieval Cross-Language Information Retrieval Machine Translation Cross-Language Named Entity Retrieval Machine Transliteration Mining Canonical Correlation Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Microsoft ResearchIndia
  2. 2.Indian Institute of TechnologyBombay

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