Additive context model for entity resolution
First Claim
1. A computer system comprising:
- at least one processor; and
memory storing;
a graph-structured knowledge base of entities connected by relationships, andinstructions that, when executed by the at least one processor, causes the computer system to perform operations comprising;
receiving a span of text from a document and a quantity of phrases from the document for the span, the phrases representing a context for the span,determining that the span refers to a quantity of candidate entities from the knowledge base,for each of the quantity of candidate entities;
providing the entity and the phrases as input to an additive context model, the context model having been trained to provide a support score for an entity-phrase pair,receiving one or more support scores from the additive context model for the entity,computing a first probability for the entity by adding the support scores together and dividing by the quantity of phrases, the first probability representing a likelihood that the context resolves to the entity,receiving a second probability representing a likelihood that the span resolves to the entity regardless of context, andcomputing a third probability for the entity by combining the first probability with the second probability, andresolving the span to an entity that has a highest third probability.
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Accused Products
Abstract
Systems and methods are disclosed for using an additive context model for entity disambiguation. An example method may include receiving a span of text from a document and a phrase vector for the span. The phrase vector may have a quantity of features and represent a context for the span. The method also includes determining a quantity of candidate entities from a knowledge base that have been referred to by the span. For each of the quantity of candidate entities, the method may include determining a support score for the candidate entity for each feature in the phrase vector, combining the support scores additively, and computing a probability that the span resolves to the candidate entity given the context. The method may also include resolving the span to a candidate entity with a highest probability.
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Citations
20 Claims
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1. A computer system comprising:
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at least one processor; and memory storing; a graph-structured knowledge base of entities connected by relationships, and instructions that, when executed by the at least one processor, causes the computer system to perform operations comprising; receiving a span of text from a document and a quantity of phrases from the document for the span, the phrases representing a context for the span, determining that the span refers to a quantity of candidate entities from the knowledge base, for each of the quantity of candidate entities; providing the entity and the phrases as input to an additive context model, the context model having been trained to provide a support score for an entity-phrase pair, receiving one or more support scores from the additive context model for the entity, computing a first probability for the entity by adding the support scores together and dividing by the quantity of phrases, the first probability representing a likelihood that the context resolves to the entity, receiving a second probability representing a likelihood that the span resolves to the entity regardless of context, and computing a third probability for the entity by combining the first probability with the second probability, and resolving the span to an entity that has a highest third probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method comprising:
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receiving a span of text from a document; receiving a phrase vector for the span, the phrase vector having a quantity of features and representing a context for the span; determining, using at least one silicon-based hardware processor, a quantity of candidate entities from a knowledge base for an ambiguous entity mention included in the span; for each of the quantity of candidate entities; determining, using the at least one silicon-based hardware processor, a support score for the candidate entity for each feature in the phrase vector, combining, using the at least one silicon-based hardware processor, the support scores additively, and computing, using the combined support scores, a probability that the span resolves to the candidate entity given the context; and resolving, using the at least one silicon-based hardware processor, the span to a candidate entity with a highest probability. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer system comprising:
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at least one hardware processor; and memory storing instructions that, when executed by the at least one processor, cause the computer system to; provide labeled data to an additive context model for training, the additive context model inferring a most likely entity for a mention given a context of the mention, the additive context model storing, for each feature, at least one support score-entity pair, generate labels for unlabeled data using the trained model, the unlabeled data comprising entity mentions with respective phrase vectors, and where each label generated by the additive context model was based on additively combining support scores, and re-train the model using the generated labels for the unlabeled data and the labeled data. - View Dependent Claims (18, 19, 20)
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Specification