Cross-Language information retrieval addresses the problem of finding information in one language in response to queries expressed in another. CLIR is sometimes called” trans lingual information retrieval”. There are three major challenges in translation-based CLIR: what to translate, how to obtain translation knowledge, and how to apply the translation knowledge.
There are also some natural language processing techniques like text processing progress.
The evaluation of an interactive CLIR system can be modelled by examining how well a CLIR system can support: (2) query formulation and translations, and (2) document selection and examination.
Probably the most noticeable achievement in CLIR is that cross-language document ranking can often achieve near 100%, or even higher. Of the retrieval effectiveness of monolingual document ranking.
There are many ways in which parallelism can help a search engine process queries faster. The two most popular approaches are index partitioning and replication.
Although document partitioning is often the right choice and scales almost linearly with the number of nodes, it can unfold its true potential only if the index data found in the individual nodes are stored in main memory or any other low-latency random-access storage medium, such as flash memory, but not if it is stored on disk. Term partitioning addresses the disk seek problem by splitting the collection into sets of terms instead of sets of documents.
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