Text Classification Seed Data Generation via Nearest-Neighbor Embeddings
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Solution Overview
Problem
The existing methods for text classification are laborious and limited by the need for manual classification of seed data, resulting in restricted accuracy due to the time-consuming process of generating a sufficiently large set of labeled examples.
Innovation Solution
Automatically generating seed data by finding nearest-neighbor vectors in a pre-trained corpus to produce a text cloud, which is used to train a text classifier, reducing manual effort and enabling more accurate and rapid classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification is used to generate seed data for text classification, then the classifier can be trained with labeled examples, but the process is laborious and time-consuming, limiting the size of the seed data set and thus the accuracy of the model
Solution Approach 1:
The system uses the pre-trained corpus and embedding vectors to automatically generate seed data without human intervention. The text cloud generation process is self-service, where the computer system autonomously identifies nearest neighbor vectors and creates labeled training examples, eliminating the need for manual classification while maintaining data quality.
Solution Approach 2:
A pre-trained corpus with embedding vectors is prepared in advance before the text classification task begins. This preliminary action creates a ready-to-use resource that enables rapid automatic generation of seed data, avoiding the need for manual classification during the actual training process.
2Reliability
If a larger seed data set is used to train the text classifier, then the model accuracy improves, but the manual effort and time required to classify samples increases proportionally
Solution Approach 1:
The manual mechanical process of classifying text samples is replaced with an automated computational system. The computer system uses embedding vectors and nearest neighbor search algorithms to automatically generate seed data, substituting human cognitive effort with machine processing that can operate at much higher speeds and scale to larger data sets.
Solution Approach 2:
The system changes the parameter of seed data generation from manual classification to automatic vector-based matching. By transforming the classification criterion into a mathematical operation on embedding vectors, the system achieves both high productivity and high reliability, generating large numbers of accurate seed examples rapidly.
3Measurement precision
If more samples are manually classified to expand the seed data set, then the text classifier can achieve higher accuracy, but the process becomes increasingly laborious and cost-prohibitive
Solution Approach 1:
Embedding vectors serve as an intermediary between the pre-trained corpus and the text classification task. Instead of directly manually classifying text samples, the system uses embedding vectors as a mediator to automatically generate seed data, simplifying the overall process while maintaining or improving accuracy.
Data Source
AI summary
Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.