Multi-label Classifier for Fine-grained Topic Extraction
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Solution Overview
Problem
Existing technologies face challenges in performing fine-grained topic extraction from unstructured text documents, particularly in sports news contexts, where attributes like injury or trade rumors lack corresponding structured data sources, necessitating a high-precision and high-recall topical classification method.
Innovation Solution
A multi-label classifier is trained using a word embedding layer and n-gram convolutional layers, with a weakly supervised training process involving unsupervised clustering and manual labeling, and further fine-tuned using seed data, enabling efficient topic prediction in text content without requiring significant human labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-label classifier with word embedding and n-gram convolutional layers is used, then topic extraction precision is improved, but model complexity increases
Solution Approach 1:
The model segments the text processing task into distinct functional components: word embedding layer for lexical representation, unigram convolutional layers for single-word pattern detection, bigram convolutional layers for phrase-level patterns, and fully connected layers for classification. This segmentation allows each component to specialize in specific aspect of topic extraction, improving overall precision while maintaining manageable complexity through modular architecture.
2Quantity of substance
If weakly supervised training with unsupervised clustering is used, then training data requirements are reduced, but labeling accuracy may deteriorate
Solution Approach 1:
The system performs preliminary unsupervised clustering on the training data to identify potential topic groups before human labeling occurs. This preliminary organization of data into clusters based on semantic similarity allows the subsequent weakly supervised labeling process to work with pre-structured information, reducing the total amount of data that requires human attention while maintaining labeling accuracy through the structured approach.
Solution Approach 2:
Unsupervised clustering acts as an intermediary between the raw training data and the final labeled data. It creates an intermediate representation of data organized by semantic similarity, which then serves as the foundation for weakly supervised labeling. This intermediary step enables the system to leverage automated organization to reduce human labeling effort while preserving accuracy through the semantic structure established by clustering.
3Measurement precision
If sentence-level labels are generated for fine-grained topic extraction, then search relevance is improved, but processing time increases
Solution Approach 1:
The system segments the document into individual sentences and applies the multi-label classifier to each sentence independently. This segmentation enables fine-grained topic extraction at the sentence level, improving search relevance by identifying specific topic-bearing sentences. The independent processing of each sentence allows for parallel computation, which mitigates the processing time increase by enabling efficient batch processing of multiple sentences simultaneously.
Data Source
AI summary
The example embodiments are directed toward improvements in document classification. In an embodiment, a method is disclosed comprising generating a set of sentences based on a document; predicting a set of labels for each sentence using a multi-label classifier, the multi-label classifier including a self-attended contextual word embedding backbone layer, a bank of trainable unigram convolutions, a bank of trainable bigram convolutions, and a fully connected layer the multi-label classifier trained using a weakly labeled data set; and labeling the document based on the set of labels. The various embodiments can target multiple use cases such as identifying related entities, trending related entities, creating ephemeral timeline of entities, and others using a single solution. Further, the various embodiments provide a weakly supervised framework to train a model when a labeled golden set does not contain a sufficient number of examples.