Information Retrieval Questions Medium
Query expansion using parallel corpora in cross-language information retrieval is a technique used to improve the accuracy and relevance of search results when users search for information in a language different from the language of the indexed documents. This process involves leveraging the similarities between languages by expanding the user's query with additional terms or phrases from a parallel corpus.
The process of query expansion using parallel corpora typically involves the following steps:
1. Collection of parallel corpora: Parallel corpora are collections of texts in two or more languages that are aligned at the sentence or phrase level. These corpora are essential for cross-language information retrieval as they provide translations of texts between languages.
2. Query translation: The user's query, expressed in the source language, needs to be translated into the target language. Machine translation techniques can be used to automatically translate the query, or manual translation can be employed if the quality of machine translation is not satisfactory.
3. Term extraction: Once the query is translated, the next step is to extract relevant terms or phrases from the parallel corpora. This can be done by aligning the translated query with the parallel corpus and identifying similar terms or phrases in the target language.
4. Term selection: The extracted terms or phrases need to be filtered and selected based on their relevance to the query and their frequency in the parallel corpus. Various statistical measures, such as term frequency-inverse document frequency (TF-IDF), can be used to determine the importance of each term.
5. Query expansion: The selected terms or phrases are then added to the translated query to expand its scope and improve the retrieval effectiveness. The expanded query now contains additional terms that are more likely to match relevant documents in the target language.
6. Retrieval and ranking: The expanded query is used to retrieve documents from the indexed collection in the target language. The retrieved documents are then ranked based on their relevance to the expanded query, using techniques such as vector space models or probabilistic models.
By incorporating query expansion using parallel corpora, cross-language information retrieval systems can overcome the language barrier and provide more accurate and relevant search results to users searching in a language different from the indexed documents. This technique leverages the linguistic similarities between languages and utilizes the wealth of information available in parallel corpora to enhance the retrieval process.