Detecting Highly Confident Word Translations from Comparable Corpora without Any Prior Knowledge

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 449–459, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Detecting Highly Confident Word Translations from Comparable Corpora without Any Prior Knowledge Ivan Vuli´c and Marie-Francine Moens Department of Computer Science KU Leuven Celestijnenlaan 200A Leuven, Belgium {ivan.vulic,marie-francine.moens}@cs.kuleuven.be Abstract In this paper, we extend the work on using latent cross-language topic models for iden- tifying word translations across compara- ble corpora. We present a novel precision- oriented algorithm that relies on per-topic word distributions obtained by the bilin- gual LDA (BiLDA) latent topic model. The algorithm aims at harvesting only the most probable word translations across lan- guages in a greedy fashion, without any prior knowledge about the language pair, relying on a symmetrization process and the one-to-one constraint. We report our re- sults for Italian-English and Dutch-English language pairs that outperform the current state-of-the-art results by a significant mar- gin. In addition, we show how to use the al- gorithm for the construction of high-quality initial seed lexicons of translations. 1 Introduction Bilingual lexicons serve as an invaluable resource of knowledge in various natural language pro- cessing tasks, such as dictionary-based cross- language information retrieval (Carbonell et al., 1997; Levow et al., 2005) and statistical machine translation (SMT) (Och and Ney, 2003). In or- der to construct high quality bilingual lexicons for different domains, one usually needs to possess parallel corpora or build such lexicons by hand. Compiling such lexicons manually is often an ex- pensive and time-consuming task, whereas the methods for mining the lexicons from parallel cor- pora are not applicable for language pairs and do- mains where such corpora is unavailable or miss- ing. Therefore the focus of researchers turned to comparable corpora, which consist of documents with partially overlapping content, usually avail- able in abundance. Thus, it is much easier to build a high-volume comparable corpus. A representa- tive example of such a comparable text collection is Wikipedia, where one may observe articles dis- cussing the similar topic, but strongly varying in style, length and vocabulary, while still sharing a certain amount of main concepts (or topics). Over the years, several approaches for min- ing translations from non-parallel corpora have emerged (Rapp, 1995; Fung and Yee, 1998; Rapp, 1999; Diab and Finch, 2000; D´ejean et al., 2002; Chiao and Zweigenbaum, 2002; Gaussier et al., 2004; Fung and Cheung, 2004; Morin et al., 2007; Haghighi et al., 2008; Shezaf and Rappoport, 2010; Laroche and Langlais, 2010), all sharing the same Firthian assumption, often called the distributionial hypothesis (Harris, 1954), which states that words with a similar meaning are likely to appear in similar contexts across languages. All these methods have examined different rep- resentations of word contexts and different meth- ods for matching words across languages, but they all have in common a need for a seed lexicon of translations to efficiently bridge the gap between languages. That seed lexicon is usually crawled from the Web or …

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