Disambiguating concepts in natural language

A computer system to improve determination of a valid sense of a concept by concept disambiguation in a natural language input, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
• receiving a plurality of text;
• determining a concept is present in the plurality of text;
• 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, and wherein determining the set of hypotheses for the determined concept comprises:
• analyzing a plurality of concept ontology data for the determined set of hypotheses corresponding to the determined concept;
• and
• associating the determined set of hypotheses with the determined concept, wherein associating the determined set of hypotheses with the determined concept is based on converting the determined concept into a word embedded vector and determining a similar vector in the analyzed plurality of concept ontology data;
• substituting the determined concept in the plurality of text with a hypothesis from the determined set of hypotheses;
• 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;
• storing the plurality of text with the determined hypothesis in place of the substituted concept based on determining that the hypothesis is valid;
• and
• displaying the stored plurality of text.

A non-transitory computer program product to improve determination of a valid sense of a concept by concept disambiguation in a natural language input, the computer program product comprising:

• one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor, the program instructions comprising:
• program instructions to receive a plurality of text;
• program instructions to determine a concept is present in the plurality of text;
• program instructions to determine a set of hypotheses for the determined concept, wherein the set of hypotheses is a plurality of natural language representations of the determined concept, and wherein program instructions to determine the set of hypotheses for the determined concept comprises:
• program instructions to analyze a plurality of concept ontology data for the determined set of hypotheses corresponding to the determined concept;
• and
• program instructions to associate the determined set of hypotheses with the determined concept, wherein program instructions to associate the determined set of hypotheses with the determined concept is based on program instructions to convert the determined concept into a word embedded vector and determining a similar vector in the analyzed plurality of concept ontology data;
• program instructions to substitute the determined concept in the plurality of text with a hypothesis from the determined set of hypotheses;
• program instructions to determine the hypothesis is valid based on analyzing the plurality of text with a neural network, wherein the neural network is trained for hypothesis validation;
• program instructions to store the plurality of text with the determined hypothesis in place of the substituted concept based on determining that the hypothesis is valid;
• and
• program instructions to display the stored plurality of text.