Abstract
We explore the potential of probabilistic topic modeling within the relevance modeling framework for both monolingual and cross-lingual ad-hoc retrieval. Multilingual topic models provide a way to represent documents in a structured and coherent way, regardless of their actual language, by means of language-independent concepts, that is, cross-lingual topics. We show how to integrate the topical knowledge into a unified relevance modeling framework in order to build quality retrieval models in monolingual and cross-lingual contexts. The proposed modeling framework processes all documents uniformly and does not make any conceptual distinction between monolingual and cross-lingual modeling. Our results obtained from the experiments conducted on the standard CLEF test collections reveal that fusing the topical knowledge and relevance modeling leads to building monolingual and cross-lingual retrieval models that outperform several strong baselines. We show that that the topical knowledge coming from a general Web-generated corpus boosts retrieval scores. Additionally, we show that within this framework the estimation of cross-lingual relevance models may be performed by exploiting only a general non-parallel corpus.
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Vulić, I., Moens, MF. (2013). A Unified Framework for Monolingual and Cross-Lingual Relevance Modeling Based on Probabilistic Topic Models. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_9
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DOI: https://doi.org/10.1007/978-3-642-36973-5_9
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