DocTag2Vec Joint Embedding for Dynamic Multi-Label Document Tagging
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Solution Overview
Problem
Existing document tagging systems are inefficient in adapting to new training data and handling dynamic sets of candidate tags, leading to suboptimal performance in predicting relevant labels for documents, especially in fast-paced environments like social media where content is generated rapidly.
Innovation Solution
The DocTag2Vec method learns joint vector representations for documents and tags in a scalable, language-independent manner, allowing for incremental updates and dynamic handling of new labels without requiring retraining, using a k-nearest neighbor search for efficient prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard multi-label learning algorithms are used with separate feature vector generation, then the system can predict tags for documents, but the system cannot incrementally adapt to new training data without retraining from scratch
Solution Approach 1:
The patent merges the feature extraction process and the classification process into a unified neural network model. The document embedding layer automatically generates feature vectors from raw text, and these features are directly fed into the multi-label classification layer. This integration allows the entire system to be trained incrementally using stochastic gradient descent, enabling the model to adapt to new training data without requiring complete retraining, thus resolving the contradiction between adaptability and time loss.
2Adaptability or versatility
If the tagging system handles a very large set of candidate tags that change dynamically, then the system can cover new topics, but the system complexity increases
Solution Approach 1:
The patent implements a dynamic tag handling mechanism where the set of candidate tags can be updated without retraining the entire model. New tags are added to the vocabulary and corresponding embedding vectors are initialized, allowing the system to adapt to new topics and dynamic tag sets. This dynamic approach reduces system complexity compared to static models that require complete retraining when tags change.
Solution Approach 2:
The patent segments the tag handling process into independent components: tag embedding vectors, tag-to-index mappings, and classification weights. This segmentation allows individual tags to be added, removed, or updated independently without affecting the entire system structure. Each tag has its own embedding vector that can be initialized or updated separately, reducing the complexity of managing large dynamic tag sets.
3Ease of operation
If traditional document tagging methods are used, then the system can label documents, but the system requires pre-generated feature vectors and cannot work directly with raw text
Solution Approach 1:
The patent merges the feature extraction function and the classification function into a single integrated neural network model. The document embedding layer takes raw text input and automatically generates feature vectors through learned embeddings, eliminating the need for separate feature generation steps. This integration simplifies the operational process while the neural network architecture efficiently handles the complexity of feature learning within the unified model structure.
Data Source
AI summary
A method implemented by at least one server computer is provided, including the following operations: receiving a plurality of training documents, each training document being defined by a sequence of words, each training document having one or more labels associated therewith; embedding the training documents, the words, and the labels in a vector space, wherein the embedding is configured to locate a given training document and its associated labels in proximity to each other in the vector space; embedding a new document in the vector space; performing a proximity search in the vector space to identify a set of nearest labels to the new document in the vector space; associating the nearest labels to the new document.