Emoji Sanitization with Semantic Token Clustering to Reduce NLP Complexity
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Solution Overview
Problem
NLP models face significant training overhead and complexity due to the large number of emojis with varying appearance modifiers, leading to increased computing resources and reduced accuracy in understanding semantic meanings.
Innovation Solution
An NLP device sanitizes emojis by generating tokens based on semantic meaning clusters, removing appearance modifiers, and providing these tokens to the NLP model, reducing the number of inputs and tokens needed for training and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP models process all emojis with appearance modifiers individually, then the models can recognize detailed emoji variations, but the training overhead and computing resources increase significantly
Solution Approach 1:
The patent merges multiple emojis with the same semantic meaning but different appearance modifiers into a single token representation. For example, emojis like 👍, 👍🏻, 👍🏼, 👍🏽, 👍🏾, 👍🏿 (thumbs up with different skin tones) are all mapped to the same token, reducing the vocabulary size and model complexity while preserving semantic understanding.
Solution Approach 2:
The patent extracts and removes appearance modifier information from emoji representations during tokenization. The system identifies and strips skin tone modifiers, color variations, and other appearance-related Unicode modifiers, keeping only the base emoji semantic meaning for model processing.
2Adaptability or versatility
If NLP models include all emoji variations with appearance modifiers in training data, then the training data comprehensively covers emoji diversity, but the training overhead increases
Solution Approach 1:
The patent creates a universal token system where a single token represents multiple emoji variations. This universal token can handle all appearance modifiers of a base emoji, making the model versatile in recognizing different emoji forms while requiring only one training example per semantic meaning rather than multiple examples for each variation.
Solution Approach 2:
The system discards appearance modifier information during tokenization (removing skin tone modifiers, color variations) but recovers the ability to handle diverse emojis by mapping them to their base form tokens. This allows comprehensive emoji coverage without proportionally increasing training data size.
3Reliability
If NLP models process emojis with appearance modifiers, then the models can understand nuanced emoji expressions, but the computing resources required increase
Solution Approach 1:
The patent segments the emoji processing task into two parts: (1) identification of the base emoji semantic meaning, and (2) removal of appearance modifier information. This segmentation allows the model to focus computational resources on understanding semantic meaning rather than processing irrelevant appearance variations.
Solution Approach 2:
The system extracts and removes appearance modifier Unicode characters from emoji sequences before processing. By taking out skin tone modifiers, color variations, and other appearance-related elements, the system reduces the computational burden while preserving the core semantic meaning needed for reliable understanding.
Data Source
AI summary
In some implementations, a device may obtain a natural language input including an emoji. The device may identify one or more appearance modifiers associated with the emoji. The device may generate a token associated with the emoji that removes the one or more appearance modifiers, wherein the token is associated with multiple emojis including the emoji, and wherein the token is a modified code associated with the emoji or is associated with a cluster that is associated with the multiple emojis. The device may provide, to a natural language processing (NLP) model, the token associated with the emoji. The device may obtain, from the NLP model, an output that indicates an interpretation of the natural language input based on providing the token to the NLP model.