Ivan Vulić
Susana Zoghbi
ABSTRACT
We study the problem of linking information between different idiomatic usages of the same language, for example, colloquial and formal language. We propose a novel probabilistic topic model called multi-idiomatic LDA (MiLDA). Its modeling principles follow the intuition that certain words are shared between two idioms of the same language, while other words are non-shared, that is, idiom-specific. We demonstrate the ability of our model to learn relations between cross-idiomatic topics in a dataset containing product descriptions and reviews. We intrinsically evaluate our model by the perplexity measure. Following that, as an extrinsic evaluation, we present the utility of the new MiLDA topic model in a recently proposed IR task of linking Pinterest pins (given in colloquial English on the users' side) to online webshops (given in formal English on the retailers' side). We show that our multi-idiomatic model outperforms the standard monolingual LDA model and the pure bilingual LDA model both in terms of perplexity and MAP scores in the IR task.
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Digital Library
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- S. Zoghbi, I. Vuli--c, and M.-F. Moens. Are words enough?: A study on text-based representations and retrieval models for linking pins to online shops. In CIKM UnstructureNLP Workshop, pages 45--52, 2013. Google ScholarDigital Library
Index Terms
- Learning to bridge colloquial and formal language applied to linking and search of E-Commerce data
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