Text Autocomplete Punctuation Prediction Neural Network
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Solution Overview
Problem
Traditional text autocomplete systems do not effectively suggest appropriate punctuation marks based on the tone and context of the text being generated, leading to suboptimal user experience and accuracy in text completion.
Innovation Solution
A data processing system utilizing natural language processing (NLP) to generate vector representations of tokens, including punctuation marks, and training an artificial neural network (ANN) to predict the appropriate use of punctuation marks in sentence structures, allowing for real-time suggestions or automatic insertion of punctuation marks during text entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text autocomplete systems are used, then the system structure is simple, but the accuracy of punctuation mark prediction is insufficient
Solution Approach 1:
The system segments the text processing task into tokenization (splitting text into words and punctuation marks), vector representation generation (creating numerical representations), and neural network prediction (predicting punctuation marks). This segmentation allows each component to focus on a specific function, improving overall accuracy while maintaining manageable complexity
Solution Approach 2:
The patent introduces an intermediary layer of vector representations that bridges the gap between raw text tokens and punctuation mark predictions. These vector representations capture contextual information and serve as effective features for the neural network, enabling accurate prediction without requiring an overly complex system architecture
2Manufacturing precision
If punctuation mark prediction is improved, then text completion quality increases, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-tokenizing the input text and pre-generating vector representations before the actual prediction task. This preparation work is done in advance and can be cached or optimized, allowing the main prediction process to run faster and reducing overall processing time while maintaining high completion quality
Solution Approach 2:
The patent changes the parameter representation from raw text to vector representations, which are optimized numerical formats that can be processed more efficiently by neural networks. This parameter transformation enables faster computation and prediction while capturing the necessary contextual information for high-quality text completion
3Adaptability or versatility
If context-aware punctuation prediction is implemented, then relevance of suggestions improves, but system complexity increases
Solution Approach 1:
The neural network model is designed to handle multiple functions: predicting punctuation marks, understanding context, and generating relevant suggestions all within a single unified model. This multi-functionality approach improves context awareness and suggestion relevance while avoiding the need for separate specialized systems that would increase overall complexity
Data Source
AI summary
A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.