Relation extraction from text using machine learning

1. A method comprising:

operating a first neural network on a processor and a memory to encode a first natural language string into a first sentence encoding comprising a set of word encodings;

adjusting, using a word-based attention mechanism with a context vector, a weight value for a word encoding within the first sentence encoding to form an adjusted first sentence encoding, a sentence layer of the first neural network comprising the word-based attention mechanism with the context vector, the word-based attention mechanism trained to form the adjusted first sentence encoding by adjusting a plurality of weights within the sentence layer;

generating, using a sentence-based attention mechanism, a first relationship encoding corresponding to the adjusted first sentence encoding, the sentence-based attention mechanism further adjusting a plurality of adjusted sentence encodings output from the word-based attention mechanism, the first relationship encoding comprising a vector representation of a relationship between entities, the relationship expressed in the first natural language string, a relation layer of the first neural network comprising the sentence-based attention mechanism, the sentence-based attention mechanism trained to generate the first relationship encoding by adjusting a plurality of weights within the relation layer;

computing an absolute difference between the first relationship encoding and a second relationship encoding; and

determining, using a multi-layer perceptron, a degree of analogical similarity between the first relationship encoding and the second relationship encoding.

2. The method of claim 1 , further comprising:

operating a second neural network on a processor and a memory to encode a second natural language string into a second sentence encoding comprising a second set of word encodings;

adjusting, using a second word-based attention mechanism with a second context vector, a weight value for a word encoding within the second sentence encoding to form an adjusted second sentence encoding; and

determining, using a second sentence-based attention mechanism, the second relationship encoding corresponding to the adjusted second sentence encoding.

3. The method of claim 2 , wherein the first neural network and the second neural network are identically structured.

4. The method of claim 2 , wherein the word-based attention mechanism with the context vector and the second word-based attention mechanism with the second context vector are identically structured.

5. The method of claim 2 , wherein the sentence-based attention mechanism and the second sentence-based attention mechanism are identically structured.

6. The method of claim 2 , further comprising:

determining, using an output unit including a sigmoid activation function, that the first relationship encoding and the second relationship encoding correspond to an analogous relationship.

7. The method of claim 2 , further comprising:

determining, using an output unit including a sigmoid activation function, that the first relationship encoding and the second relationship encoding do not correspond to an analogous relationship.

8. The method of claim 2 , further comprising:

training, using a set of pairs of natural language strings, wherein each natural language string in the set of pairs of natural language strings expresses a relationship between entities included in the natural language string, the first neural network and the second neural network.

9. The method of claim 8 , further comprising:

generating a set of relation pairs, wherein each relation pair in the set of relation pairs comprises a pair of entities and a relationship relating the pair of entities;

generating a set of positive example pairs, wherein each positive example pair comprises two relation pairs, a relationship of each relation pair in the set of positive example pairs being equivalent to each other;

generating a set of negative example pairs, wherein each negative example pair comprises two relation pairs, a relationship of each relation pair in the set of negative example pairs not being equivalent to each other;

combining, forming a training set of example pairs, the set of positive example pairs and the set of negative example pairs; and

converting, by extracting from a text corpus a natural language string expressing a relationship between entities included in the natural language string, the training set of example pairs to a training set of pairs of natural language strings.

10. A computer usable program product comprising one or more computer-readable storage media, and program instructions stored on at least one of the one or more computer-readable storage media, the stored program instructions comprising:

program instructions to operate a first neural network on a processor and a memory to encode a first natural language string into a first sentence encoding comprising a set of word encodings;

program instructions to adjust, using a word-based attention mechanism with a context vector, a weight value for a word encoding within the first sentence encoding to form an adjusted first sentence encoding, a sentence layer of the first neural network comprising the word-based attention mechanism with the context vector, the word-based attention mechanism trained to form the adjusted first sentence encoding by adjusting a plurality of weights within the sentence layer;

program instructions to generate, using a sentence-based attention mechanism, a first relationship encoding corresponding to the adjusted first sentence encoding, the sentence-based attention mechanism further adjusting a plurality of adjusted sentence encodings output from the word-based attention mechanism, the first relationship encoding comprising a vector representation of a relationship between entities, the relationship expressed in the first natural language string, a relation layer of the first neural network comprising the sentence-based attention mechanism, the sentence-based attention mechanism trained to generate the first relationship encoding by adjusting a plurality of weights within the relation layer;

program instructions to compute an absolute difference between the first relationship encoding and a second relationship encoding; and

program instructions to determine, using a multi-layer perceptron, a degree of analogical similarity between the first relationship encoding and the second relationship encoding.

11. The computer usable program product of claim 10 , further comprising:

program instructions to operate a second neural network on a processor and a memory to encode a second natural language string into a second sentence encoding comprising a second set of word encodings;

program instructions to adjust, using a second word-based attention mechanism with a second context vector, a weight value for a word encoding within the second sentence encoding to form an adjusted second sentence encoding; and

program instructions to determine, using a second sentence-based attention mechanism, the second relationship encoding corresponding to the adjusted second sentence encoding.

12. The computer usable program product of claim 11 , wherein the first neural network and the second neural network are identically structured.

13. The computer usable program product of claim 11 , wherein the word-based attention mechanism with the context vector and the second word-based attention mechanism with the second context vector are identically structured.

14. The computer usable program product of claim 11 , wherein the sentence-based attention mechanism and the second sentence-based attention mechanism are identically structured.

15. The computer usable program product of claim 11 , further comprising:

program instructions to determine, using an output unit including a sigmoid activation function, that the first relationship encoding and the second relationship encoding correspond to an analogous relationship.

16. The computer usable program product of claim 11 , further comprising:

program instructions to determine, using an output unit including a sigmoid activation function, that the first relationship encoding and the second relationship encoding do not correspond to an analogous relationship.

17. The computer usable program product of claim 11 , further comprising:

program instructions to train, using a set of pairs of natural language strings, wherein each natural language string in the set of pairs of natural language strings expresses a relationship between entities included in the natural language string, the first neural network and the second neural network.

18. The computer usable program product of claim 10 , wherein the stored program instructions are stored in a computer-readable storage medium in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.

19. The computer usable program product of claim 10 , wherein the stored program instructions are stored in a computer-readable storage medium in a server data processing system, and wherein the stored program instructions are downloaded over a network to a remote data processing system for use in a computer readable storage medium associated with the remote data processing system.

20. A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage media, and program instructions stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, the stored program instructions comprising:

program instructions to operate a first neural network on a processor and a memory to encode a first natural language string into a first sentence encoding comprising a set of word encodings;

program instructions to adjust, using a word-based attention mechanism with a context vector, a weight value for a word encoding within the first sentence encoding to form an adjusted first sentence encoding, a sentence layer of the first neural network comprising the word-based attention mechanism with the context vector, the word-based attention mechanism trained to form the adjusted first sentence encoding by adjusting a plurality of weights within the sentence layer;

program instructions to generate, using a sentence-based attention mechanism, a first relationship encoding corresponding to the adjusted first sentence encoding, the sentence-based attention mechanism further adjusting a plurality of adjusted sentence encodings output from the word-based attention mechanism, the first relationship encoding comprising a vector representation of a relationship between entities, the relationship expressed in the first natural language string, a relation layer of the first neural network comprising the sentence-based attention mechanism, the sentence-based attention mechanism trained to generate the first relationship encoding by adjusting a plurality of weights within the relation layer;

program instructions to compute an absolute difference between the first relationship encoding and a second relationship encoding; and

program instructions to determine, using a multi-layer perceptron, a degree of analogical similarity between the first relationship encoding and the second relationship encoding.