NLP Model Adaptation via Key Term Replacement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural-language processing (NLP) models struggle to detect new or infrequently used terminology in user-generated content, leading to false negatives and requiring time-consuming retraining with updated datasets.
Innovation Solution
The technique involves preprocessing inputs by replacing new terminology with broader category names, which are then trained into the NLP model as atomic units, allowing for quicker detection without retraining, by using a map file to substitute new terms with category names and processing these categories as known terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the NLP model is trained with updated datasets to detect new terminology, then detection accuracy improves, but training time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing input text to replace new terminology with known category names before the NLP model processes it. This allows the model to detect new terms through their category representations without requiring retraining, thus improving detection accuracy while avoiding time-consuming retraining cycles
Solution Approach 2:
A map file serves as an intermediary between new terminology and the NLP model. The map file stores mappings between new terms and existing category names, allowing the system to translate unknown terms into a format the model can process without direct retraining, thereby resolving the contradiction between detection accuracy and training time
2Measurement precision
If the NLP model processes new terminology directly, then detection accuracy improves, but computation overhead increases
Solution Approach 1:
The system extracts new terminology from input text before processing and replaces it with references to existing category names. This extraction and substitution process removes the computational burden of processing entirely new terms while preserving detection capability through category-based matching, thus reducing computation overhead while maintaining detection accuracy
3Adaptability or versatility
If the NLP model is retrained frequently to incorporate new terminology, then adaptability improves, but system complexity increases
Solution Approach 1:
Instead of retraining the complex NLP model to incorporate new terminology, the system creates a simplified copy or representation of new terms in the map file that maps to existing categories. This copying approach allows the system to adapt to new terminology by simply adding entries to the map file, maintaining high adaptability while avoiding the complexity of frequent model retraining
Data Source
AI summary
Techniques are disclosed relating to storing, by a computer system, a map file that includes a new term that is mapped to a particular one of a plurality of category names that a natural-language processing (NLP) model is trained to identify, wherein the NLP model is not trained to identify the new term. The technique may further include receiving, by the computer system, user-generated content (UGC) that includes the new term. Additionally, the technique may include generating, by the computer system using the map file, a modified version of the UGC by substituting the particular category name for text in the UGC that is associated with the new term, and using, by the computer system, the NLP model on the modified text to classify the modified version of the UGC without retraining the NLP model to identify the new term.