Identifying a stale data source to improve NLP accuracy
First Claim
1. A system, comprising:
- a computer processor; and
a memory containing a program that, when executed on the computer processor, performs an operation comprising;
receiving a query for processing by a natural language processing (NLP) system comprising a corpus containing data ingested from a plurality of data sources, wherein the data is formatted and stored into one or more objects and organized based on topic changes, and wherein the ingestion is performed by at least one hardware resource of the NLP system;
identifying a data source expected to contain an answer to the query using NLP, by;
dividing words in the query into different elements,generating an annotation for each of the elements using the NLP system by determining a particular topic describing each of the elements, andidentifying a previously ingested data source in the corpus that is associated with previously-generated annotations matching the generated annotations for the elements;
upon determining that the previously ingested data in the corpus does not contain the answer to the query, determining whether new material has been added to the identified data source since the last time the identified data source was ingested into the corpus;
upon determining that new material has been added to the identified data source since the last time the identified data source was ingested into the corpus;
re-ingesting the identified data source whereby the new material is inserted into the corpus; and
processing the query to determine a lexical answer type for the query, based at least in part on a concept assigned to each of the elements, wherein the concepts were determined and assigned using NLP, and wherein the lexical answer type is a word or noun phrase that predicts a type of an answer to the query; and
generating an answer to the query based on the new material inserted into the corpus and based on the lexical answer type.
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Accused Products
Abstract
In some NLP systems, queries are compared to different data sources stored in a corpus to provide an answer to the query. However, the best data sources for answering the query may not currently be contained within the corpus or the data sources in the corpus may contain stale data that provides an inaccurate answer. When receiving a query, the NLP system may evaluate the query to identify a data source that is likely to contain an answer to the query. If the data source is not currently contained within the corpus, the NLP system may ingest the data source. If the data source is already within the corpus, however, the NLP may determine a time-sensitivity value associated with at least some portion of the query. This value may then be used to determine whether the data source should be re-ingested—e.g., the information contained in the corpus is stale.
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Citations
15 Claims
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1. A system, comprising:
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a computer processor; and a memory containing a program that, when executed on the computer processor, performs an operation comprising; receiving a query for processing by a natural language processing (NLP) system comprising a corpus containing data ingested from a plurality of data sources, wherein the data is formatted and stored into one or more objects and organized based on topic changes, and wherein the ingestion is performed by at least one hardware resource of the NLP system; identifying a data source expected to contain an answer to the query using NLP, by; dividing words in the query into different elements, generating an annotation for each of the elements using the NLP system by determining a particular topic describing each of the elements, and identifying a previously ingested data source in the corpus that is associated with previously-generated annotations matching the generated annotations for the elements; upon determining that the previously ingested data in the corpus does not contain the answer to the query, determining whether new material has been added to the identified data source since the last time the identified data source was ingested into the corpus; upon determining that new material has been added to the identified data source since the last time the identified data source was ingested into the corpus; re-ingesting the identified data source whereby the new material is inserted into the corpus; and processing the query to determine a lexical answer type for the query, based at least in part on a concept assigned to each of the elements, wherein the concepts were determined and assigned using NLP, and wherein the lexical answer type is a word or noun phrase that predicts a type of an answer to the query; and generating an answer to the query based on the new material inserted into the corpus and based on the lexical answer type. - View Dependent Claims (2, 3, 4, 5, 6, 14)
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7. A computer program product for maintaining a corpus in a natural language processing (NLP) system, the computer program product comprising:
a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code comprising computer-readable program code configured to; receive a query for processing by the NLP system comprising a corpus containing data ingested from a plurality of data sources, wherein the data is formatted and stored into one or more objects and organized based on topic changes, and wherein the ingestion is performed by at least one hardware resource of the NLP system; identify a data source expected to contain an answer to the query using NLP, by; dividing words in the query into different elements, generating an annotation for each of the elements using the NLP system by determining a particular topic describing each of the elements, and identifying a previously ingested data source in the corpus that is associated with previously-generated annotations matching the generated annotations for the elements; upon determining that the previously ingested data in the corpus does not contain the answer to the query, determine whether new material has been added to the identified data source since the last time the identified data source was ingested into the corpus; upon determining that new material has been added to the identified data source since the last time the identified data source was ingested into the corpus; re-ingesting the identified data source whereby the new material is inserted into the corpus; and processing the query to determine a lexical answer type for the query, based at least in part on a concept assigned to each of the elements, wherein the concepts were determined and assigned using NLP, and wherein the lexical answer type is a word or noun phrase that predicts a type of an answer to the query; and generate an answer to the query based on the new material inserted into the corpus and based on the lexical answer type. - View Dependent Claims (8, 9, 10, 11, 12, 13, 15)
Specification