A major challenge in Cross-Language Information Retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects the term weighting which is known to highly impact the retrieval performance. In this paper, we present an analytical study of using translation knowledge in CLIR. In particular, by adopting axiomatic analysis framework, we formulate the impacts of translation knowledge on document ranking as constraints that any cross-language retrieval model should satisfy. We then consider the state-of-the-art CLIR methods and check whether they satisfy these constraints. Finally, we show through empirical evaluation that violating one of the constraints harms the retrieval performance significantly which calls for further investigation.
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Axiomatic Analysis of Cross-Language Information Retrieval
Semantic Scholar · Computer Science · 2014
Abstract
A major challenge in Cross-Language Information Retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects the term weighting which is known to highly impact the retrieval performance. In this paper, we present an analytical study of using translation knowledge in CLIR. In particular, by adopting axiomatic analysis framework, we formulate the impacts of translation knowledge on document ranking as constraints that any cross-language retrieval model should satisfy. We then consider the state-of-the-art CLIR methods and check whether they satisfy these constraints. Finally, we show through empirical evaluation that violating one of the constraints harms the retrieval performance significantly which calls for further investigation.