Abstract: This disclosure relates to method and system for improving relevancy and ranking of a search result for a search query. The method may include accessing documents of the search result. Each document may be associated with derived document features that may be derived based on an evaluation of document features with respect to query features. For each document, the method may further include determining a feature score for each of the derived document features based on one or more of the query features and initializing each element of an order array with the feature score for each ordered document feature. The ordered document features may be the derived document features that are ordered based on pre-defined rules. The method may further include ranking the documents by comparing and ranking the order array for each of the documents and presenting an updated search result based on the ranking of the documents.

Abstract: This disclosure relates generally to an information retrieval technology and more particularly to a creation of a taxonomy to facilitate subsequent search and retrieval of information. In one embodiment, an information retrieval device is disclosed, that comprises a processor and a memory that stores instructions, which, on execution, causes the processor to receive an input corpus. Thereafter, input document clusters are generated from top input n-grams associated with the input corpus. Further, top-ranked input n-grams are determined from the top input n-grams. Thereafter, an external corpus is identified based on the top-ranked input n-grams. An enriched corpus (external and input corpus), is clustered based on top enriched n-grams associated with the enriched corpus to generate enriched document clusters. Further, for each n-gram of the enriched corpus, corresponding n-gram clusters are determined.

Abstract: This disclosure relates to method and system for improving relevancy and ranking of a search result for a search query. The method may include accessing documents of the search result. Each document may be associated with derived document features that may be derived based on an evaluation of document features with respect to query features. For each document, the method may further include determining a feature score for each of the derived document features based on one or more of the query features and initializing each element of an order array with the feature score for each ordered document feature. The ordered document features may be the derived document features that are ordered based on pre-defined rules. The method may further include ranking the documents by comparing and ranking the order array for each of the documents and presenting an updated search result based on the ranking of the documents.

Abstract: This disclosure relates to method and system for improving relevancy and ranking of a search result from an index-based search for a given search query. The method may include accessing a number of documents of the search result. Each of the documents may be associated with a number of document natural language (NL) feature metadata, a number of document indexing metadata, and at least one document class. The method may further include determining at least one query class, a number of query NL feature metadata, and a number of query indexing metadata for the given search query. The method may further include determining at least one of a relevancy and a ranking of each of the documents using a set of pre-defined rules, and presenting an updated search result based on the at least one of the relevancy and the ranking of each of the documents.

Abstract: This disclosure relates generally to an information retrieval technology and more particularly to a creation of a taxonomy to facilitate subsequent search and retrieval of information. In one embodiment, an information retrieval device is disclosed, that comprises a processor and a memory that stores instructions, which, on execution, causes the processor to receive an input corpus. Thereafter, input document clusters are generated from top input n-grams associated with the input corpus. Further, top-ranked input n-grams are determined from the top input n-grams. Thereafter, an external corpus is identified based on the top-ranked input n-grams. An enriched corpus (external and input corpus), is clustered based on top enriched n-grams associated with the enriched corpus to generate enriched document clusters. Further, for each n-gram of the enriched corpus, corresponding n-gram clusters are determined.