Patent Document ID: 9141601
Application ID: 13303258

Base Claim:
1. A learning device comprising: a memory that stores computer executable units; and a processor configured to execute the computer executable units stored in the memory; an input receiving unit, executed by the processor, that receives an input of training data representing a sentence, rear boundaries of elements having an anaphoric relation within the sentence, and a correspondence relation between elements that are an antecedent and an anaphor; a first generation unit, executed by the processor, that generates example data by using the training data, the example data representing a first rear boundary, a second rear boundary, and a label which indicates whether or not an element represented by the first rear boundary and an element represented by the second rear boundary have the anaphoric relation; an inferring unit, executed by the processor, that infers a range of the element represented by the first rear boundary and a range of the element represented by the second rear boundary by inferring front boundaries of the element represented by the first rear boundary and the element represented by the second rear boundary based on a predetermined rule and replaces the first rear boundary and the second rear boundary of the example data with texts of the inferred ranges to generate expanded example data; a second generation unit, executed by the processor, that generates a feature vector based on the expanded example data; and a learning unit, executed by the processor, that learns criteria through machine learning by using the feature vector, the criteria being a weight vector used for determining whether or not there is the anaphoric relation in an arbitrary sentence.

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Claim 2:
2. The learning device according to claim 1 , further comprising: a plurality of the inferring units, executed by the processor, that infer the ranges by inferring the front boundaries based on the predetermined rules that are different from each other and generates a plurality of expanded example data; a plurality of the second generation units, executed by the processor, that generate a plurality of the feature vectors based on the plurality of the expanded example data; and an integration unit, executed by the processor, that integrates the plurality of the feature vectors, wherein the learning unit learns the criteria through machine learning by using the feature vectors that are integrated.