Text Categorization Using Inferred Semantic Labels
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Solution Overview
Problem
Natural language processing (NLP) frameworks face challenges due to the limited availability of training data and the difficulty in effectively integrating semantic features, making it resource-intensive to train reliable NLP models.
Innovation Solution
The use of automatically-generated training data based on Word Mover's Distance (WMD) measures and supervised text categorization models, which determine inferred semantic labels for unlabeled documents by leveraging prior labels and cross-document similarity measures, enables efficient and semantically-informed training of NLP models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supervised text categorization models are trained with limited labeled training data, then the model training process becomes resource-intensive and computationally expensive, but the model reliability and accuracy deteriorate
Solution Approach 1:
The system performs preliminary unsupervised clustering on unlabeled training data to generate inferred semantic labels before the supervised training phase. This preliminary action creates a larger effective training dataset without requiring additional human annotation resources, thereby improving model reliability while avoiding increased training resource consumption.
Solution Approach 2:
The system enables the training data to self-service by automatically generating inferred semantic labels through unsupervised clustering algorithms. This self-labeling mechanism eliminates the need for external human annotation resources, allowing the system to scale training data without proportionally increasing resource consumption.
2Measurement precision
If more labeled training data is collected to improve model accuracy, then the training data availability increases, but the data annotation cost and time consumption increase
Solution Approach 1:
The system performs preliminary unsupervised clustering to generate inferred semantic labels for unlabeled data before the supervised training phase. This preliminary action transforms unlabeled data into labeled training data automatically, improving categorization accuracy without incurring additional data annotation time.
Solution Approach 2:
The system creates copies of labeled data patterns by clustering similar unlabeled documents together and assigning them inferred labels based on their cluster centroids. This copying mechanism effectively multiplies the utility of existing labeled data without requiring additional human annotation time.
3Reliability
If semantic features are effectively integrated into NLP models to improve text categorization quality, then the categorization effectiveness improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary unsupervised clustering to organize unlabeled data by semantic similarity before supervised training. This preliminary semantic organization creates a structured training dataset that enables effective semantic feature integration without significantly increasing model complexity, as the semantic structure is already established in the training data.
Data Source
AI summary
There is a need for more effective and efficient text categorization. This need can be addressed by, for example, techniques for semantic text categorization. In one example, a method includes determining an input vector-based representation of an input document; processing the input vector-based representation using a trained supervised machine learning model to generate the categorization based at least in part on the input vector-based representation, wherein: (i) the trained supervised machine learning model has been trained using automatically-generated training data, and (ii) the automatically generated training data is generated by determining an inferred semantic label for each unlabeled training document of one or more unlabeled training documents; and performing one or more categorization-based actions based at least in part on the categorization, and (iii) the labels are described by one or more short documents/short texts.