System and method for entity extraction from semi-structured text documents
First Claim
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1. An automated method for extracting entities from a text document comprising:
- for at least a section of a text document,extracting a first set of entities in predefined classes of entity from the at least a section, the extraction of the first set of entities comprising at least one of a rule-based extraction method and a probabilistic extraction method;
identifying a location of each of the extracted entities in the at least a section of the document;
clustering at least a subset of the extracted entities in the first set into clusters, based on the identified locations of the entities in the document;
identifying complete clusters of entities and incomplete clusters of entities from the clusters, based on correlations observed between sequences of entities in the clusters and a number of the classes of entity within each entity cluster;
learning patterns for extracting new entities based on the complete clusters; and
extracting new entities from the incomplete clusters based on the learned patterns,wherein the extracting of the first set of entities, identifying complete clusters, learning patterns, and extracting new entities are performed with a processor device.
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Abstract
A method for extracting entities from a text document includes, for at least a section of a text document, providing a first set of entities extracted from the at least a section, clustering at least a subset of the extracted entities in the first set into clusters, based on locations of the entities in the document. Complete ones of the clusters of entities are identified. Patterns for extracting new entities are learned based on the complete clusters. New entities are extracted from incomplete clusters based on the learned patterns.
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Citations
20 Claims
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1. An automated method for extracting entities from a text document comprising:
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for at least a section of a text document, extracting a first set of entities in predefined classes of entity from the at least a section, the extraction of the first set of entities comprising at least one of a rule-based extraction method and a probabilistic extraction method; identifying a location of each of the extracted entities in the at least a section of the document; clustering at least a subset of the extracted entities in the first set into clusters, based on the identified locations of the entities in the document; identifying complete clusters of entities and incomplete clusters of entities from the clusters, based on correlations observed between sequences of entities in the clusters and a number of the classes of entity within each entity cluster; learning patterns for extracting new entities based on the complete clusters; and extracting new entities from the incomplete clusters based on the learned patterns, wherein the extracting of the first set of entities, identifying complete clusters, learning patterns, and extracting new entities are performed with a processor device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A system for extracting entities from text documents comprising:
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a first entity extraction component for extracting a first set of entities from at least a section of a text document, each of the extracted entities being in one of a predefined set of entity classes; a second entity extraction component for extraction of new entities from the at least the section of the text document, the second entity extraction component comprising; a clustering component for clustering at least a subset of the extracted entities in the first set into clusters, based on locations of the entities in the document and their entity classes, a cluster completeness component for identifying complete clusters of entities and incomplete clusters of entities from the clusters, based on correlations observed between sequences of entity classes in the clusters, and a pattern recognition component for learning patterns of the entity classes for extracting new entities based on the complete clusters and extracting new entities from the incomplete clusters based on the learned patterns; and a processor for implementing the first and second entity extraction components, clustering component, cluster completeness component, and pattern recognition component. - View Dependent Claims (19)
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20. A method for extracting entities from a resume comprising:
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segmenting the resume into sections; extracting a first set of entities and respective entity class labels from the section with at least one of grammar rules, a probabilistic model, and a lexicon; clustering at least a subset of the extracted entities in the first set into clusters, based on locations of the entities in the resume; identifying complete clusters of entities and incomplete clusters of entities from the clusters, based on correlations observed between sequences of entity class labels in the clusters; learning patterns for extracting new entities based on the class labels of the entities in the complete clusters; extracting new entities from the incomplete clusters, based on the learned patterns; and outputting information based on the extracted new entities in the resume, wherein the segmenting, extracting the first set of entities, clustering, identifying complete clusters, learning patterns, and extracting new entities are performed with a processor device.
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Specification