Textual input analysis methods and systems for determining degree of correctness

1 . A method for adaptively processing a textual input to determine factual correctness, comprising:

receiving, by a backend system via an interface operating on a computing device, the textual input;

analyzing, by the backend system, the received textual input to generate one or more fact-related textual fragments;

generating, by the backend system, one or more search strings based on the generated one or more fact-related textual fragments, wherein the generation of the one or more search strings comprises concatenating keywords using at least one of:

Boolean operators,

field annotations, and

literature type indicators;

querying, by the backend system, at least one search database with the one or more search strings to retrieve a plurality of documents comprising textual content relevant to factual claims or statements in the textual input;

segregating, by the backend system, relevant fact-related textual corpus from each of the retrieved plurality of documents;

comparing, by the backend system, the one or more fact-related textual fragments with one or more corresponding fact-related textual corpus;

categorizing, by the backend system using at least one artificial intelligence algorithm, the received textual input into at least one degree-of-correctness category based on the comparing;

adapting, by the backend system, the at least one artificial intelligence algorithm based on information related to results of the categorizing, wherein the adapting enhances an accuracy of the at least one artificial intelligence algorithm; and

utilizing the adapted at least one artificial intelligence algorithm to perform the categorizing of one or more subsequent textual inputs.

2 . The method of claim 1 , further wherein:

the categorizing comprises categorizing at least one fact-related textual fragment into one degree-of-correctness category; and

the method further comprises:

generating a visualization data structure that associates the at least one fact-related textual fragment with:

its degree-of-correctness category,

a color corresponding to the degree-of-correctness category associated with the at least one fact-related textual fragment, and

links to one or more retrieved documents; and

transmitting the visualization data structure to the computing device for rendering as an interactive factual correctness visualization.

3 . The method of claim 2 , further comprising causing, based on user interaction with the factual correctness visualization, the computing device to generate and display a pop-up comprising an explanation of the categorizing of the at least one fact-related textual fragment.

4 . The method of claim 1 , further comprising receiving user feedback on the categorizing.

5 . The method of claim 4 , further wherein the adapting of the at least one artificial intelligence algorithm comprises:

adjusting, based on the received user feedback, one or more parameters related to the at least one artificial intelligence algorithm.

6 . The method of claim 5 , further comprising: utilizing Term Frequency-Inverse Document Frequency (TF-IDF) to determine critical words; and

the generation of the one or more search strings performed using the determined critical words.

7 . The method of claim 5 , wherein:

the one or more Boolean operators are selected from a group comprising AND, OR, NOT, and NEAR; and

the one or more field annotations are selected from a group comprising:

title,

abstract,

author, and

publication date.

8 . The method of claim 5 , further comprising:

refining the one or more search strings by cross-referencing the one or more strings with at least one of:

one or more encyclopedia pages, and

one or more search engine outputs.

9 . A system for factual verification with adaptive artificial intelligence parameter adjustment, comprising:

an interface operating on a computing device communicatively coupled to a backend system, wherein:

the backend system comprises one or more processors communicatively coupled to at least one memory storing one or more instructions,

the backend system is communicatively coupled to at least one search database,

the interface obtains a textual input, and

the backend system receives the textual input from the computing device; and

the one or more processors execute the one or more instructions to:

analyze the textual input received by the backend system to generate one or more fact-related textual fragments, wherein the analyzing further comprises:

tokenizing the textual input to produce one or more tokens based on one or more sentences or smaller chunks, and

identifying, using the one or more tokens and a Named Entity Recognition (NER) algorithm, one or more sentences or fragments that contain factual claims or statements,

generate one or more search strings based on the one or more fact-related textual fragments,

query the at least one search database with the one or more search strings to retrieve a plurality of documents comprising textual content relevant to factual claims or statements in the textual input;

segregate a relevant fact-related textual corpus from each of the plurality of retrieved documents,

compare the one or more fact-related textual fragments with the corresponding segregated relevant fact-related textual corpus,

categorize, using at least one artificial intelligence algorithm, the textual input into at least one category,

receive user feedback related to the categorization via the interface,

adjust, based on the user feedback, one or more parameters related to the at least one artificial intelligence algorithm to enhance an accuracy of the categorization, and

utilize the at least one artificial intelligence algorithm with the adjusted one or more parameters to categorize one or more subsequent textual inputs.

10 . The system of claim 9 , wherein the one or more processors execute the one or more instructions to:

refine the one or more search strings by cross-referencing the one or more search strings with one or more encyclopedia pages and one or more search engine outputs.

11 . The system of claim 10 , wherein the segregating of the relevant fact-related textual corpus comprises using at least one of:

predefined keywords,

contextual clues,

regular expression matching,

string-matching techniques,

sentiment analysis, and

natural language processing.

12 . The system of claim 10 , wherein:

the comparison of the fact-related textual fragments with the segregated relevant fact-related textual corpus is based on at least one similarity metric, further wherein:

the at least one similarity metric is based on at least one of:

a cosine similarity and

a Jaccard index.

13 . The system of claim 10 , wherein:

the comparison of the fact-related textual fragments with the segregated relevant fact-related textual corpus is based on at least one of:

keyword matching, and

semantic analysis.

14 . The system of claim 9 , wherein:

the one or more fact-related textual fragments comprises a first fact-related textual fragment; and

the one or more processors execute the one or more instructions to:

generate a prompt corresponding to the first fact-related textual fragment, wherein:

the prompt is structured to elicit a factual verification response.

15 . The system of claim 14 , wherein:

the generation of the prompt is performed using context related to the first fact-related textual fragment, wherein the context is based on at least one of:

the first fact-related textual fragment,

the received textual input,

one or more pages of search engine results, and

one or more encyclopedia pages.

16 . The system of claim 15 , wherein the generation of the one or more search strings comprises concatenating keywords using at least one of:

Boolean operators,

field annotations, and

literature type indicators.

17 . The system of claim 16 , wherein:

the Boolean operators are selected from a group comprising AND, OR, NOT, and NEAR; and

the field annotations are selected from a group comprising:

title,

abstract,

author, and

publication date.

18 . The system of claim 15 , wherein:

the one or more processors execute the one or more instructions to determine critical words utilizing Term Frequency-Inverse Document Frequency (TF-IDF); and

the generation of the one or more search strings is performed using the determined critical words.

19 . The system of claim 9 , wherein:

the one or more fact-related textual fragments comprises a first fact-related textual fragment;

the categorization comprises categorization of the first fact-related textual fragment into one degree-of-correctness category;

the one or more processors execute the one or more instructions to:

generate a visualization data structure that associates the first fact-related textual fragment with:

its degree-of-correctness category,

a color corresponding to the degree-of-correctness category associated with the first fact-related textual fragment, and

links to one or more retrieved documents.

20 . The system of claim 19 , wherein:

the one or more processors execute the one or more instructions to transmit the visualization data structure to the computing device;

the transmitting of the visualization data structure causes the interface to display:

the first fact-related textual fragment rendered using the color corresponding to the degree-of-correctness category,

an explanatory message for the degree-of-correctness category, and

the links to the one or more retrieved documents.