Cross-language Information Retrieval Approach Research Articles

A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

AbstractCross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.

IJARCCE - Computer and Communication Engineering

In this paper we present a fully implemented system which supports cross-lingual search of World Wide Web. Cross-language information retrieval (CLIR) is a subfield of information retrieval (IR) that deals with the retrieval of the information in a language different from the language of the user's query. Google is a powerful tool to search information on the World Wide Web. Google is a keyword based search engine. Google is a widely used search engine that uses the keyword matching to find the documents that are important & relevant to user's query. It will retrieve the documents as a search result that contains query terms. Search words have multiple meanings or appear in multiple contexts or in multiple languages. Semantically similar pages that are desirable by the user are often not retrieved .CLIR is helpful for refining the search result of Google. It allows the search engine to not only retrieve the documents containing the query terms but also the documents containing the semantic equivalent of query terms in the other languages. The system, which is named as Refinement of Google Search Results by Cross Lingual Reference technique will improve the quality of the search results of Google by providing the multilingual documents in response to a user query written in single language. The goal of this system is to improve the search result of Google by using the CLIR approach for search. This System will avoid the overhead involved in creating multiple requests in case of different languages document retrieval .This System can be used for NLP Linguistics, Govt Organizations such as Intelligence dept, Immigration dept.,Multilingual population regions etc.

A multilingual financial watch alerting system

AbstractDepending on user profiles expressed by associated expected events, and conditions for raising alerts, from manual meticulous news annotation of an adequately selected corpus, an ontology domain is created. A cross-language information retrieval approach is used for automatic translation of financial documents corresponding to the particular domain corresponding to the user requirement. By this way, users may receive alerts and news expressed in their own language even if they are initially expressed in a different language. Manual annotation is used for knowledge extraction composed of general rules useful for automatic annotation of financial news arriving instantly to the system from reliable providers of information. As a first step in the loop, news are filtered, split into different sub-documents each one corresponding to a particular event and categorized. News are then mapped automatically to formatted data as instantiations of a sequence of predefined entities defined an event. By using a...