Sentence Boundary Detection Using Phrase Scoring and ML
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Solution Overview
Problem
Current NLP models struggle with determining sentence boundaries in text data lacking proper punctuation, leading to erroneous results and resource wastage due to inefficient processing of text with improper punctuation.
Innovation Solution
A detection system utilizing machine learning models for sentence boundary detection, which extracts phrases, calculates question and answer scores, and adds punctuation to form valid sentences, thereby improving the accuracy of NLP models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If NLP models process text data without proper punctuation, then they can handle raw text directly, but they generate erroneous results and consume excessive computing resources
Solution Approach 1:
The system performs sentence boundary detection and punctuation insertion as a preliminary step before NLP processing. By detecting sentence boundaries using machine learning models and inserting appropriate punctuation marks beforehand, the text data is prepared in advance to ensure both processing efficiency and result accuracy in subsequent NLP operations
Solution Approach 2:
The system introduces an intermediary component that detects sentence boundaries and inserts punctuation between the raw text input and the NLP model processing. This intermediary layer processes the text to add structural information (sentence boundaries and punctuation) without requiring manual intervention, thereby improving both productivity and reliability
2Measurement precision
If manual punctuation is added to text data, then sentence boundaries are accurate, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables text data to self-correct by automatically detecting sentence boundaries and inserting punctuation through machine learning models. The text processing system serves itself by identifying its own structural needs and fulfilling them without external human intervention, achieving high accuracy while maintaining efficiency
Solution Approach 2:
The system replaces the mechanical process of manual punctuation insertion with an automated machine learning-based detection system. Instead of human operators manually adding punctuation, the system uses trained models to detect sentence boundaries and insert punctuation automatically, dramatically reducing time loss while maintaining precision
Data Source
AI summary
A device may receive text data, may extract phrases from the text data, and may calculate question scores for the phrases. The device may determine whether the phrases are associated with single answers or multiple answers, may calculate, single answer scores for the phrases associated with the single answers, and may calculate multiple answer scores for the phrases associated with the multiple answers. The device may extract, from the phrases, phrase data identifying grammar data, start tag data, and end tag data, and may process the phrase data, the single answer scores, and the multiple answer scores, with a machine learning model, to generate valid sentence scores for the text data. The device may identify, as a valid sentence, a group of phrases of the text data associated with a highest one of the valid sentence scores, and may add punctuation to the valid sentence to generate a final sentence.