Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: A method is provided that includes generating answer-passage pairs, each associated with a respective one of multiple feature vectors. The method includes, for each answer in the pairs, merging the feature vectors associated with any of the pairs that include the answer to obtain a merged answer vector. The method includes, for each passage in the pairs, (i) merging the feature vectors associated with any of the pairs that includes the passage to obtain a merged passage vector, (ii) merging the feature vectors from the merged answer vector for each answer that is associated with the passage via at least one of the pairs to obtain a merged passage-answer vector, and (iii) concatenating the merged passage vector and the merged passage-answer vector to obtain a concatenated passage vector. The method includes ranking the concatenated passage vector for each passage to obtain a ranked list of passages with associated confidence scores.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: A method is provided that includes generating answer-passage pairs, each associated with a respective one of multiple feature vectors. The method includes, for each answer in the pairs, merging the feature vectors associated with any of the pairs that include the answer to obtain a merged answer vector. The method includes, for each passage in the pairs, (i) merging the feature vectors associated with any of the pairs that includes the passage to obtain a merged passage vector, (ii) merging the feature vectors from the merged answer vector for each answer that is associated with the passage via at least one of the pairs to obtain a merged passage-answer vector, and (iii) concatenating the merged passage vector and the merged passage-answer vector to obtain a concatenated passage vector. The method includes ranking the concatenated passage vector for each passage to obtain a ranked list of passages with associated confidence scores.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.