Grammatical Context Detection for Text Attribute Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text mining techniques require significant user input or training data for accurate extraction of attribute values from unstructured text, and often fail to capture valid attribute values, especially in unstructured data, due to their dependency on classification methods that may ignore relevant information.
Innovation Solution
A computer-implemented method and system that uses grammatical context detection techniques to extract attribute values from text data by associating part-of-speech tags, identifying attribute descriptors using approximate text matching, and parsing tagged information files to determine attribute descriptors without the need for training data, making it domain-independent and applicable to both structured and unstructured text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification techniques are used to extract attribute values from text data, then extraction accuracy may be improved in some cases, but relevant data may be ignored or lost
Solution Approach 1:
The patent introduces grammatical context as an intermediary mechanism between the text data and attribute extraction process. Part-of-speech tagging and grammatical analysis serve as mediators that identify potential attribute values without pre-classifying the entire text, thereby preserving valid values that classification methods might discard.
Solution Approach 2:
The system enables the text data itself to identify its own attribute values through inherent grammatical structures. By analyzing part-of-speech tags and grammatical context within the text, the method allows the data to self-identify relevant attributes without external classification imposition, reducing information loss.
2Measurement precision
If supervised machine-learning methods are used for text mining, then extraction accuracy may be improved, but significant user input or training data is required
Solution Approach 1:
The patent replaces the mechanical system of supervised machine-learning training with a linguistic analysis approach. Instead of requiring extensive training data and user input to teach the system, it uses grammatical context detection and part-of-speech tagging—natural language processing techniques that inherently understand text structures without training.
Solution Approach 2:
The method allows the text processing system to automatically determine attribute values through grammatical analysis without requiring external training or user guidance. The system serves itself by utilizing the inherent grammatical structures present in the text data.
3Measurement precision
If domain-specific classification methods are used, then extraction accuracy for specific domains may be improved, but the solution becomes less adaptable to other domains
Solution Approach 1:
The patent creates a universal attribute extraction method based on grammatical context that can function across multiple domains. By relying on fundamental linguistic structures (part-of-speech tags, grammatical relationships) rather than domain-specific patterns, the system achieves multi-functionality and adaptability to different text domains while maintaining extraction accuracy.
Data Source
AI summary
Systems and method for extracting attributes from text content are described. Example embodiments may include a computer implemented method for extracting attributes from text data, wherein the text data is obtained from at least one information source. As described, the implementation may include receiving, from a user, an address for the at least one information source and an attribute name, creating a tagged information file by associating a part of speech tag to text data obtained from the at least one information source, identifying a location of the attribute name in the tagged information file using an approximate text matching technique and determining at least one attribute descriptor from the tagged information file wherein the tagged information file is parsed based on a part of speech tag associated with the attribute name to determine a conclusion of the attribute descriptor.