Phonetic Pattern Matrix for NLP Fuzzy Matching
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Solution Overview
Problem
Current NLP algorithms face challenges in accurately understanding natural language tokens due to phonetic variations, dialects, and accents, leading to incorrect selections and low precision in fuzzy matching, as they primarily rely on textual misspellings and do not effectively utilize phonetic characteristics.
Innovation Solution
The method involves extracting phonetic patterns from natural language inputs, converting them into syllable sequences, and storing these in a matrix for use in a modified fuzzy matching algorithm, allowing for improved precision by considering phonetic similarities and structural similarities between tokens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fuzzy matching algorithms are designed to have high recall, then more token variations are captured, but precision deteriorates due to false positives
Solution Approach 1:
The patent segments tokens into phonetic components (syllables) and represents them as sequences of phonetic units. By dividing tokens into manageable phonetic segments, the system can compare individual syllables rather than treating entire tokens as monolithic units, enabling more precise matching while maintaining comprehensive coverage of variations.
Solution Approach 2:
The patent transforms the matching parameter from character-based textual comparison to phonetic pattern-based comparison. By changing the representation parameter from spelling to phonetic transcription, the system can capture spoken variations, dialects, and accents while maintaining precise matching through structured phonetic sequence comparison.
2Measurement precision
If phonetic patterns are extracted and stored in a matrix for fuzzy matching, then matching precision is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary extraction and normalization of phonetic patterns from tokens before the matching process. By pre-processing tokens into standardized phonetic sequences and storing them in a structured matrix format, the system prepares data in advance for efficient comparison, reducing the computational complexity during actual matching operations.
Solution Approach 2:
The patent creates phonetic pattern copies or representations of original tokens and stores these phonetic versions in a separate matrix structure. This copying approach allows the system to work with simplified phonetic representations during matching while preserving the original token data, separating the complexity of phonetic analysis from the matching logic.
Data Source
AI summary
A token is extracted from a Natural Language input. A phonetic pattern is computed corresponding to the token, the phonetic pattern including a sound pattern that represents a part of the token when the token is spoken. New data is created from data of the phonetic pattern, the new data including a syllable sequence corresponding to the phonetic pattern. A state of a data storage device is changed by storing the new data in a matrix of syllable sequences corresponding to the token. An option is selected that corresponds to the token by executing a fuzzy matching algorithm using a processor and a memory, the selecting of the option is based on a syllable sequence in the matrix.