NLP Intent Map Matching for Poorly Structured Text
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Solution Overview
Problem
Existing Natural Language Processing (NLP) techniques struggle with accurately identifying intents and purposes from user inputs, especially when the inputs are poorly structured or poorly worded, due to limited and biased training data, and the inability to capture patterns in varying languages and dialects.
Innovation Solution
A method and system that generate input intent maps using NLP models, processing user inputs through intent map transforming algorithms to match with pre-stored intent maps, determining the closest match, and rendering a response, which requires only a single training input and uses iterative and elastic stretching processes to manipulate intent maps, allowing for accurate intent identification regardless of input quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP techniques with labeled training examples are used, then intent classification works well for standard patterns, but it fails for poorly structured or poorly-worded texts that do not follow expected patterns
Solution Approach 1:
Instead of trying to make poorly structured texts fit expected patterns through traditional parsing, the patent inverts the approach by using embedding models to represent both the input text and training examples in a continuous vector space where semantic similarity can be measured directly, allowing flexible matching without rigid structural requirements
Solution Approach 2:
The patent changes the parameter space from discrete grammatical categories and syntactical rules to continuous embedding vectors, allowing the system to capture semantic meaning and intent regardless of the specific linguistic structure or wording used in the input text
2Measurement precision
If linguistic processing with grammar and parser is used, then grammatical and syntactical understanding is achieved, but it does not work well for texts that do not pass through grammatical processing and for varying languages and dialects
Solution Approach 1:
The embedding model serves multiple functions simultaneously: it captures semantic meaning, handles grammatical understanding, and adapts to varying languages and dialects through a single unified representation space, eliminating the need for separate processing pipelines for each function
Solution Approach 2:
The patent replaces the mechanical grammatical parsing system with a neural embedding-based semantic matching system that operates on continuous vector representations, allowing it to handle linguistic variations without relying on rigid grammatical rules
3Measurement precision
If more training data is collected for better neural network training, then intent classification improves, but the training data becomes biased and over-fit or under-fit occurs
Solution Approach 1:
The patent uses embedding models to create vector representations that capture the essential semantic features of training examples without requiring large amounts of raw text data, effectively copying the meaningful patterns into a compact numerical form that generalizes better
Solution Approach 2:
The patent transforms the training problem from learning from raw text patterns to learning from embedding vector relationships, changing the parameter space to one where semantic similarity is naturally captured and bias is reduced
4Measurement precision
If virtual assistant services with tagged training data are used, then intent recognition is improved, but it is challenging to determine where the ML process failed and hard to resolve mismatch between user intent and NLP derived intent
Solution Approach 1:
The patent implements a feedback mechanism where the system calculates similarity scores between the embedding of the user's input text and embeddings of all training examples, then ranks and returns the closest matches along with their similarity scores, providing transparent feedback on how the intent was determined and where mismatches may exist
Data Source
AI summary
The disclosure relates to system and method for processing user input using Natural Language Processing (NLP). The method includes generating, by an NLP model, a set of input intent maps associated with a user input. The method includes matching each of the set of input intent maps with each of a plurality of pre-stored sets of intent maps. Each of the plurality of pre-stored sets of intent maps is generated from a single predefined training input and is mapped to a predefined intent and a predetermined response. The method includes determining a distance of each of the set of input intent maps relative to each of the plurality of pre-stored sets of intent maps. Further, the method includes identifying a pre-stored intent map closest to the set of input intent maps and rendering the predetermined response mapped to the pre-stored sets of intent maps to the user.