Context-Aware Phrase Extraction for Multi-Context Text
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Solution Overview
Problem
Conventional methods fail to accurately extract insightful phrases from textual information, particularly in cases involving single or multiple contexts, due to issues with pattern recognition, context dependency, and ambiguity, leading to incomplete or inaccurate understanding of customer feedback.
Innovation Solution
A framework is developed that includes an input text preprocessor, multiple context detector, context dependency detector, context bounding determiner, phrase extraction engine, and phrase selection model to identify and separate independent contexts, apply linguistic features, and select the most insightful phrases using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pattern recognition methods are used to extract phrases from text, then the extraction process is simple and fast, but the accuracy and insightfulness of extracted phrases deteriorates
Solution Approach 1:
The system segments the text processing task into multiple specialized components: context detection module identifies different contexts in text, context bounding module determines boundaries of contextual information, and phrase extraction module extracts phrases specific to each context. This segmentation allows each component to specialize in one aspect, improving overall extraction accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces context as an intermediary concept between raw text and extracted phrases. The context detection and context bounding modules act as intermediaries that process raw text to identify and delimit contextual regions, which then guide the phrase extraction process. This intermediary layer enables more accurate phrase extraction by providing contextual guidance without requiring the entire system to be overly complex.
2Reliability
If multiple contexts in text are not separated, then the processing is simpler, but the understanding of customer feedback becomes inaccurate
Solution Approach 1:
The system segments text into multiple independent contexts using the context detection module, which identifies distinct contextual regions within the input text. The context bounding module then determines precise boundaries for each context. This segmentation allows the system to handle multiple contexts separately, improving feedback understanding accuracy by ensuring phrases are extracted from the correct contextual region rather than mixing information from different contexts.
Solution Approach 2:
The system performs partial action by focusing on extracting phrases from specific contextual regions rather than processing the entire text uniformly. The context bounding determiner identifies only the relevant portions of text for each context, allowing the phrase extraction engine to concentrate computational resources on extracting meaningful phrases from bounded contexts, thereby improving accuracy without proportionally increasing overall complexity.
3Loss of information
If context dependency is not considered, then the extraction process is faster, but the extracted phrases lose semantic meaning
Solution Approach 1:
The system performs preliminary action by detecting contexts and bounding contextual regions before the actual phrase extraction process. The context detection module identifies contextual regions in advance, and the context bounding module establishes boundaries beforehand. This preliminary processing preserves semantic information by ensuring phrases are extracted with their contextual context intact, while the pre-established boundaries prevent unnecessary processing during the extraction phase, balancing information retention with processing efficiency.
Data Source
AI summary
The present teaching relates to extracting insightful phrases from an input text. Independent contexts are first identified from the input text. With respect to each independent context, initial candidate phrases are generated with respect to linguistic features and are then filtered. Various features are then computed for each filtered candidate phrase and used to select top k candidate phrases for each independent context. A most insightful phrase is then selected from the k top candidate phrases using deep learned models. Such selected most insightful phrases for the independent contexts are then used for facilitating an understanding the input text.