Unsupervised relation detection model training
First Claim
1. A method of automatically generating natural language patterns based on a knowledge graph, the method comprising:
- selecting a relation from a knowledge graph;
extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation;
receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words;
extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words;
extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet;
generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation;
generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation;
generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and
applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system.
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Abstract
A relation detection model training solution. The relation detection model training solution mines freely available resources from the World Wide Web to train a relationship detection model for use during linguistic processing. The relation detection model training system searches the web for pairs of entities extracted from a knowledge graph that are connected by a specific relation. Performance is enhanced by clipping search snippets to extract patterns that connect the two entities in a dependency tree and refining the annotations of the relations according to other related entities in the knowledge graph. The relation detection model training solution scales to other domains and languages, pushing the burden from natural language semantic parsing to knowledge base population. The relation detection model training solution exhibits performance comparable to supervised solutions, which require design, collection, and manual labeling of natural language data.
79 Citations
18 Claims
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1. A method of automatically generating natural language patterns based on a knowledge graph, the method comprising:
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selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet; generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation; generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation; generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer readable storage device containing computer executable instructions which, when executed by a computer, perform a method for training a relation detection model without supervision, the method comprising:
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selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet; generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation; generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation; generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system. - View Dependent Claims (9, 10)
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11. A system comprising at least one processor in electronic communication with a computer readable storage device, the computer readable storage device storing instructions that, when executed, are capable of performing a method, the method comprising:
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selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet; generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation; generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation; generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A method of automatically generating natural language patterns for a knowledge graph, the method comprising:
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selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; and associating the at least one textual snippet with the relation to form a set of natural language patterns for the knowledge graph. - View Dependent Claims (18)
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Specification