Contextual Tracker Model for Textual Data Analysis
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Solution Overview
Problem
Existing techniques for identifying keywords in textual data, such as sales records, face limitations in accuracy due to reliance on predefined keyword searches and lack of contextual understanding, leading to incomplete or irrelevant keyword identification.
Innovation Solution
A method and system using a machine learning-based tracker model that generates a classification model to identify keywords or phrases with specific contexts by training on labeled samples, allowing for accurate classification of contextually relevant trackers in textual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword search based on predefined dictionary is used, then identification speed is improved, but identification accuracy deteriorates
Solution Approach 1:
The patent transforms the identification approach from static keyword matching to dynamic contextual analysis by changing the parameters considered (from single word presence to sentence-level semantic understanding). The system evaluates multiple parameters including word context, sentence structure, and semantic relationships to improve identification accuracy while maintaining efficiency through automated scoring mechanisms.
2Measurement precision
If contextual understanding is implemented, then identification accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the contextual understanding task into manageable components: sentence extraction, keyword identification within sentences, context scoring, and ranking. By dividing the complex analysis into discrete steps with clear inputs and outputs, the system achieves accurate contextual identification without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that bridges raw text analysis and final keyword identification. The context score acts as a mediator that quantifies the relevance of each potential keyword based on its surrounding text, providing a systematic way to handle complexity without requiring direct human judgment for each case.
3Loss of information
If manual review of all records is performed, then identification completeness is improved, but time consumption deteriorates
Solution Approach 1:
The patent applies partial action by focusing computational resources on identifying and analyzing only the most relevant sentences and keywords rather than processing every word in every record. By using scoring thresholds and ranking mechanisms, the system achieves comprehensive identification of important information without the time cost of exhaustive manual review of all textual data.
Data Source
AI summary
A method and system for generating a tracker model for identification of trackers in textual data are provided. The method includes receiving an input query including at least an input sentence exemplifying a tracker of interest, wherein the tracker is at least one word with a specific context; generating a base results set including a set of sentences substantially matching the input sentence, wherein the sentences in the base results set are obtained from an index indexing textual data; deriving a first labeling set from the base results set, wherein includes samples of sentences from the base results set; receiving labels on each sentence in the first labeling set; and feeding the labels to a machine learning algorithm to train the tracker model, wherein the tracker model is generated and ready when enough labels have been processed by the machine learning algorithm.