Neural Text Segmentation via Context Attention
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Solution Overview
Problem
Traditional text segmentation systems are limited by laborious feature engineering and the inability to accurately capture semantic relationships, leading to less accurate predictions due to the inclusion of irrelevant information from surrounding sentences.
Innovation Solution
A neural network system specifically trained for text segmentation using a modified hierarchical recurrent neural network (HRNN) framework with attention, similarity matching, and topic-segmentation joint label mechanisms to focus on relevant context and discount irrelevant information, improving prediction accuracy by leveraging similarity between sentences and cue-words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature engineering and breakpoint detection are used for text segmentation, then the system is simple to implement, but the segmentation accuracy is limited due to laborious feature engineering and inability to capture semantic relationships
Solution Approach 1:
The patent replaces traditional mechanical feature engineering approaches with a neural network-based semantic analysis system. The neural network automatically learns semantic relationships and topic transitions from text data, substituting the manual feature extraction and breakpoint detection pipeline with an intelligent model that captures contextual meaning and improves segmentation accuracy.
Solution Approach 2:
The neural network system performs self-training and automatic feature learning from the text data without requiring manual feature engineering. The model independently identifies semantic relationships, topic transitions, and segmentation points through its internal learning mechanisms, eliminating the need for laborious preprocessing and feature selection.
2Measurement precision
If conventional neural network models equally leverage information from surrounding sentences to predict topics, then the model is simple to train, but irrelevant sentences are included leading to less accurate predictions
Solution Approach 1:
The patent applies local quality by differentiating the treatment of surrounding sentences based on their relevance to the target sentence. The model assigns different weights to context sentences, giving higher importance to locally relevant sentences and lower importance to irrelevant ones. This localized weighting approach improves prediction accuracy by focusing computational resources on meaningful contextual information.
Solution Approach 2:
The model dynamically adjusts the weight of context sentences based on their relevance to the target sentence. Rather than using fixed equal weights, the system adaptively modulates the influence of each surrounding sentence through learned attention mechanisms, allowing the model to flexibly respond to varying contextual relevance across different positions in the text.
Data Source
AI summary
Methods and systems are provided for identifying subparts of a text. A neural network system can receive a set of sentences that includes context sentences and target sentences that indicate a decision point in a text. The neural network system can generate context vector sentences and target sentence vectors by encoding context from the set of sentences. These context sentence vectors can be weighted to focus on relevant information. The weighted context sentence vectors and the target sentence vectors can then be used to output a label for the decision point in the text.