Translingual Information Retrieval: Learning from Bilingual Corpora (ai Journal Special Issue: Best of Ijcai-97)

Translingual information retrieval (TLIR) consists of providing a query in one language and searching document collections in one or more diierent languages. This paper introduces new TLIR methods and reports on comparative TLIR experiments with these new methods and with previously reported ones in a realistic setting. Methods fall into two categories: query translation and statistical-IR approaches establishing translingual associations. The results show that using bilingual corpora for automated extraction of term equivalences in context outperforms dictionary-based methods. Translingual versions of the Generalized Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) also perform well, as does translingual pseudo relevance feedback (PRF) and Example-Based Term-in-context Translation (EBT). All showed relatively small performance loss between monolingual and translingual versions, ranging between 87% to 101% of monolingual IR performance. Query translation based on a general machine-readable bilingual dictionary { heretofore the most popular method { did not match the performance of other, more sophisticated methods. Also, the previous very high LSI results in the literature based on \mate-nding"were superseded by more realistic relevance-based evaluations ; LSI performance proved comparable to that of other statistical corpus-based methods.