Disambiguating concepts in natural language
View Patent ↗A computer receives a plurality of text and determines a concept is present in the plurality of text. The computer determines a set of hypotheses for the determined concept, wherein the set of hypotheses is a plurality of natural language representations of the determined concept. The computer substitutes the determined concept in the plurality of text with a hypothesis from the determined set of hypotheses. The computer determines the hypothesis is valid based on analyzing the plurality of text with a neural network, wherein the neural network is trained for hypothesis validation. Based on determining that the hypothesis is valid, the computer storing the plurality of text with the determined hypothesis in place of the substituted concept and displays the stored plurality of text.
1. A processor-implemented method for concept disambiguation in a natural language input, the method comprising:
receiving a plurality of text;
determining a concept in the plurality of text, wherein the concept is determined using a word embedding, wherein the word embedding converts the concept to a vector;
determining a set of hypotheses for the determined concept, wherein the set of hypotheses is a plurality of natural language representations of the determined concept, wherein the plurality of natural language representations are determined based on searching an embedded concept ontology data with the vector;
analyzing a plurality of concept ontology data for the determined set of hypotheses corresponding to the determined concept;
associating the determined set of hypotheses with the determined concept based on analyzing the plurality of concept ontology data;
substituting the determined concept in the plurality of text with a hypothesis from the determined set of hypotheses, wherein the hypothesis is selected from the determined set of hypotheses based on prioritizing the hypothesis with a longer surface forms;
determining the hypothesis is valid based on analyzing the plurality of text with a neural network, wherein the neural network is trained for hypothesis validation using positive training examples having an example concept and negative training examples, wherein the negative training examples are based on substituting randomly the example concept of the positive training example with a syntactically invalid concept;
storing the plurality of text with the determined hypothesis in place of the substituted concept based on determining that the hypothesis is valid;
displaying the stored plurality of text;
determining a next hypothesis from the determined set of hypotheses for the determined concept;
based on determining the next hypothesis, determining that the next hypothesis is valid based on analyzing the plurality of text with the neural network;
storing the plurality of text with the determined next hypothesis based on determining that the next hypothesis is valid; and
displaying the plurality of text with the next hypothesis.