Text Classification CLI for Rapid, Accurate Dataset Labeling
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Solution Overview
Problem
Existing data science classification modeling methods for large datasets are cumbersome, prone to errors, and inefficient, particularly in manual labeling processes that are cognitively demanding and slow, with potential for typos and inaccurate search results.
Innovation Solution
A computer user interface tool with a processor framework that assists in labeling datasets by filtering, training models, and computing confidence scores, including reinforcement learning to improve accuracy, and supports non-technical users in building classification models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification is performed line by line in a spreadsheet, then labeling can be done for small datasets, but it becomes extremely burdensome and error-prone for large datasets
Solution Approach 1:
The system enables self-service labeling by training a classification model on a small subset of manually labeled data, then using this model to automatically classify the remaining large portions of data. This eliminates the need for manual line-by-line classification of entire datasets while maintaining accuracy through iterative model refinement on user-corrected predictions.
Solution Approach 2:
The system performs preliminary classification using a trained model before final user verification. This preliminary action pre-processes the data by providing predicted labels that users can then review and correct, significantly reducing the time required compared to starting from scratch with manual labeling of all data.
2Quantity of substance
If manual labeling is performed on large datasets, then comprehensive labeling coverage is achieved, but cognitive fatigue and errors increase significantly
Solution Approach 1:
The system implements feedback loops where user corrections to model predictions are fed back into the training dataset. The model is retrained iteratively on this expanded labeled data, continuously improving its accuracy and reliability as it processes larger quantities of data, thereby maintaining high labeling reliability across large datasets.
Solution Approach 2:
The classification model serves as an intermediary between raw data and final labels. Instead of users directly labeling all data points, the model provides predicted labels as intermediaries that users verify and correct, reducing cognitive load and errors while achieving comprehensive labeling coverage.
3Ease of operation
If search queries are performed in worksheets, then data can be filtered and searched, but typos in queries lead to unrepresentative and inaccurate results
Solution Approach 1:
The system replaces manual text-based search queries with an automated classification model that understands semantic meaning. Instead of users typing search queries that are prone to typos, the model automatically classifies data points based on trained patterns, eliminating typo-related errors while maintaining ease of operation through simple category selection.
4Quantity of substance
If worksheets are used for large chunks of text, then all data can be displayed, but processing speed becomes extremely slow due to UI updates
Solution Approach 1:
The system segments the large text processing task into two parts: (1) the classification model quickly processes large chunks of text in the background without UI updates, and (2) only the resulting labels and confidence scores are presented to the user. This segmentation maintains the ability to process large amounts of text while achieving high speed by eliminating continuous UI refreshes during processing.
Data Source
AI summary
A text classification command line interface tool assists a user label a dataset. The command line interface tool comprises a plurality of top-level modules programmed and operable to assist a user to obtain the dataset from a file location, semi-automatically search and label select datapoints of the dataset, train a first label-assist model, compute confidence scores for each of the datapoints of the dataset, and review metrics. The command line interface tool is operable to assist a user to retrain the first label-assist model via reinforcement learning until accuracy is sufficient. The CLI tool can save and export the pre-processed labeled dataset. In embodiments, a production classifier is trained using the preprocessed dataset and operable to classify new datapoints in real time. Examples of datapoints to classify include without limitation entity websites. Examples of categories include, without limitation, SHAFT and cannabis-related categories. Related computer-implemented methods are also described.