A system and method for making entailment inferences are disclosed. Entailment inferences are computed between semantic representations of text objects which, for a set of features, indicate whether the feature is known or unknown about the text object. A function of the semantic representations of first and second text objects is computed with an asymmetric vector space operator which differs depending on the entailment relationship.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for making an entailment inference, comprising: with a processor, computing an entailment inference for a first text object with respect to a second text object, comprising computing a function of semantic representations of the first and second text objects with an asymmetric vector space operator; and outputting information based on the inference. 2. The method of claim 1, further comprising generating at least one of the semantic representations with a semantic model trained on training text sequences labeled with entailment relations. 3. The method of claim 2, further comprising training the semantic model with the labeled training text sequences. 4. The method of claim 1, wherein the first and second text objects are extracted from an input text sequence. 5. The method of claim 1, wherein the asymmetric vector space operator is selected from: a forward-inference entailment operator for predicting whether the first text object, having a semantic representation X is entailed by the second text object, having a semantic representation Y as a function of a dot product of X and log Y, where log Y is a vector in which each element is the loge of the corresponding element in Y; a backward-inference entailment operator for predicting whether the second text object entails the first text object as a function of a dot product of (1−Y) and log(1−X); and a factorized entailment operator for predicting whether an entailment relationship exists from the text object having the semantic representation Y to the text object having the semantic representation X. 6. The method of claim 5, wherein the factorized entailment operator is parameterized according to: YX=Σk log(1−σ(−Yk)σ(Xk)), where σ represents the sigmoid function and Yk and Xk are the kth elements of semantic representations Y and X, respectively. 7. The method of claim 1, wherein the making of the entailment inference comprises modeling a probability of entailment using a mean-field approximation. 8. The method of claim 1, wherein the inference comprises inferring at least one of: whether the first text object entails the second text object; whether the second text object entails the first text object; a semantic representation of the second text object, given that an entailment relation exists between the first and second text objects; and a semantic representation of the first text object, given that an entailment relation exists between the first and second text objects. 9. The method of claim 1, wherein constraints on the non-independence of elements of the semantic representations are enforced by the function. 10. The method of claim 9, wherein the constraints are incorporated in a constraint vector for predicting one of: the semantic representation X of the first text object according to: X=σ(θx+log Y), and the semantic representation Y of the second text object according to: Y=σ(θy+−log(1−X)), where represents a sigmoid function which outputs a value between 0 and 1, θx and θy are the constraint vectors which represent prior information about elements x and y of vectors X and Y, respectively. 11. The method of claim 1, further comprising parametrizing the first and second representations by their log-odds. 12. The method of claim 1, wherein the asymmetric vector space operator is selected from: a forward-inference entailment operator for predicting whether the first text object, having a semantic representation X is entailed by the second text object, having a semantic representation Y as a function of: a dot product of X and log Y, where log Y is a vector in which each element is the loge of the corresponding element in Y, or a dot product of σX and log σY, where log σY is a vector in which each element is the loge of the application of σ to the corresponding element in Y and σ represents the sigmoid function; a backward-inference entailment operator for predicting whether the second text object entails the first text object as a function of: a dot product of (1−Y) and log(1−X), or a dot product of σ(−Y) and log σ(−X); and a factorized entailment operator for predicting whether the second text object entails the first text object as a function of a sum, over a set of k dimensions, of a log function of the kth elements of X and Y. 13. The method of claim 1, wherein each of the semantic representations comprises a multidimensional vector, each dimension of the multidimensional vector represents whether something that is knowable about the text object is known or unknown. 14. The method of claim 1, further comprising clustering semantic representations of a set of text objects, based on the inferences. 15. A computer program product comprising a non-transitory recording medium storing instructions, which when executed on a computer, causes the computer to perform the method of claim 1. 16. A system comprising memory which stores instructions for performing the method of claim 1 and a processor in communication with the memory for executing the instructions. 17. An entailment system comprising: a representation generation component which generates semantic representations of text objects using a trained semantic model, each semantic representation comprising a multidimensional vector; an inference component which computes an entailment inference for a first text object with respect to a second text object, comprising computing a function of semantic representations of the first and second text objects with an asymmetric vector space operator; an output component which outputs information based on the inference; and a processor which implements the components. 18. The entailment system of claim 17, further comprising: a learning component which learns the semantic model using labeled training data. 19. The entailment system of claim 17, further comprising: a clustering component which clusters semantic representations of a set of text objects, based on the inferences. 20. A method for making an entailment inference, comprising: storing an asymmetric vector space operator for computing a function of semantic representations of first and second text objects, the asymmetric vector space operator being selected from: a forward-inference entailment operator for predicting whether the first text object, having a semantic representation X is entailed by the second text object, having a semantic representation Y as a function of a dot product of X and log Y, or sigmoid functions thereof, where log Y is a vector in which each element is the loge of the corresponding element in Y; a backward-inference entailment operator for predicting whether the second text object entails the first text object as a function of a dot product of (1−Y) and log(1−X), or sigmoid functions thereof; and a factorized entailment operator for predicting whether the second text object entails the first text object as a function of a sum, over a set of k dimensions, of a log function of the kth elements of X and Y; with a processor, computing the selected asymmetric vector space operator to infer at least one of: whether the first text object entails the second text object, whether the second text object entails the first text object, a semantic representation of the second text object, given that an entailment relation exists between the first and second text objects; a semantic representation of the first text object, given that an entailment relation exists between the first and second text objects; and outputting information based on the inference. 21. The method of claim 20, wherein the forward-inference entailment operator is of the form: XY=X·log Y, or XY≡σ(X)·log σ(Y); and wherein the backward-inference entailment operator is of the form: YX=(1−Y)·log(1−X), or YX≡σ(−Y)·log σ(−X), or is a factorized entailment operator of the form: Y   X ≡ ∑ k   log  ( 1 - ( 1 - Y k )  ( X k ) ) , or Y   X ≡ ∑ k   log  ( 1 - σ  ( - Y k )  σ  ( X k ) ) .