The Use of Monolingual Context Vectors for Missing Translations in Cross-Language Information Retrieval

For cross-language text retrieval systems that rely on bilingual dictionaries for bridging the language gap between the source query language and the target document language, good bilingual dictionary coverage is imperative. For terms with missing translations, most systems employ some approaches for expanding the existing translation dictionaries. In this paper, instead of lexicon expansion, we explore whether using the context of the unknown terms can help mitigate the loss of meaning due to missing translation. Our approaches consist of two steps: (1) to identify terms that are closely associated with the unknown source language terms as context vectors and (2) to use the translations of the associated terms in the context vectors as the surrogate translations of the unknown terms. We describe a query-independent version and a query-dependent version using such monolingual context vectors. These methods are evaluated in Japanese-to-English retrieval using the NTCIR-3 topics and data sets. Empirical results show that both methods improved CLIR performance for short and medium-length queries and that the query-dependent context vectors performed better than the query-independent versions.