Normalized Phrase Table for NLP Efficiency
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Solution Overview
Problem
Natural language processing systems face inefficiencies in processing and matching phrases due to the large number of un-normalized variants, which increases processing requirements and reduces accuracy in applications like machine translation and language identification.
Innovation Solution
The implementation of a normalized phrase table using lexicographic normalization rules, where each normalized phrase is associated with multiple un-normalized variants, allowing for weight assignment based on distance measures and probability calculations, enables efficient phrase lookup and model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple un-normalized phrase variants are stored separately in the phrase table, then information about each variant is preserved, but processing complexity and storage requirements increase
Solution Approach 1:
The patent merges multiple un-normalized phrase variants under a single normalized key in the phrase table. Instead of storing each variant separately, the normalized form serves as a unified entry point that represents all its variants, thereby reducing processing complexity while preserving information about the variants through associated metadata or lookup mechanisms.
Solution Approach 2:
The normalized phrase acts as an intermediary between the diverse un-normalized variants and the processing system. By normalizing phrases before storage or lookup, the system uses this intermediate representation to simplify matching and retrieval operations, reducing the complexity of directly handling multiple variant forms.
2Loss of information
If multiple un-normalized phrase variants are stored separately in the phrase table, then information about each variant is preserved, but storage space requirements increase
Solution Approach 1:
The patent combines multiple un-normalized phrase variants into a single normalized entry in the phrase table. This merging reduces the total number of stored entries and associated storage space, while the system preserves information about the variants through the normalization process and potential variant tracking mechanisms.
3Reliability
If phrase matching is performed on un-normalized variants, then all variants are captured, but matching accuracy decreases due to variability
Solution Approach 1:
The patent applies normalization as a preliminary action before phrase matching or storage. By converting all un-normalized variants into their normalized forms beforehand, the system ensures consistent matching criteria while still capturing all variants through the normalization process. This preliminary standardization improves matching accuracy without sacrificing the ability to recognize all variant forms.
Data Source
AI summary
Systems, methods, and apparatuses including computer program products are provided for training machine learning systems. In some implementations, a method is provided. The method includes receiving a collection of phrases, normalizing a plurality of phrases of the collection of phrases, the normalizing being based at least in part on lexicographic normalizing rules, and generating a normalized phrase table including a plurality of key-value pairs, each key value pair includes a key corresponding to a normalized phrase and a value corresponding to one or more un-normalized phrases associated with the normalized key, each un-normalized phrase having one or more parameters.