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Learning to Bridge Colloquial and Formal Language Applied to Linking and Search of E-Commerce Data

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. We demonstrate the ability of our model to learn relations between cross-idiomatic topics in a dataset containing product descriptions and reviews. We present the utility of the new MiLDA topic model in a recently proposed information retrieval task of linking Pinterest pins to online webshops . 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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Learning to Bridge Colloquial and Formal Language Applied to Linking and Search of E-Commerce Data

ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ‘14)

Ivan Vulic, Susana Zoghbi and Sien Moens

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Susana Zoghbi

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