Robert bosch gmbh (20240346364). Co-attentive Fusion with Unified Label Graph Representation for Low-resource Text Classification simplified abstract
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Co-attentive Fusion with Unified Label Graph Representation for Low-resource Text Classification
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Co-attentive Fusion with Unified Label Graph Representation for Low-resource Text Classification - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346364 titled 'Co-attentive Fusion with Unified Label Graph Representation for Low-resource Text Classification
Simplified Explanation: The patent application describes a text classification framework called co-attentive fusion with unified label graph representation (CoFuLaG). This framework involves constructing a unified label graph in the first stage, which combines structured and unstructured knowledge to enhance label semantics in text classification. In the second stage, a text classification model uses the unified label graph to predict output labels for input texts.
- **Key Features and Innovation:**
- Two-stage text classification process - Construction of a unified label graph - Incorporation of structured and unstructured knowledge - Explicit modeling of label relations - Improved label semantics in text classification
- **Potential Applications:**
- Natural language processing - Information retrieval - Sentiment analysis - Document categorization
- **Problems Solved:**
- Enhancing label semantics in text classification - Clarifying subtle differences between labels - Identifying exceptional sub-concepts under a label
- **Benefits:**
- Improved accuracy in text classification - Enhanced understanding of label relations - Better handling of complex label semantics
- **Commercial Applications:**
- SEO optimization for content categorization - E-commerce product recommendation systems - Social media content moderation tools
- **Questions about Text Classification Framework:**
* **How does the unified label graph improve label semantics in text classification?**
- The unified label graph combines structured and unstructured knowledge to provide more accurate label semantics.
* **What are the potential applications of the CoFuLaG framework beyond text classification?**
- The framework can be applied to various fields such as sentiment analysis and document categorization.
- **Frequently Updated Research:**
Ongoing research in the field of text classification focuses on improving the efficiency and scalability of models like CoFuLaG. Researchers are exploring new ways to incorporate external knowledge sources into text classification frameworks for enhanced performance.
Original Abstract Submitted
a text classification framework is disclosed, referred to as co-attentive fusion with unified label graph representation (cofulag). the text classification framework is a two-stage process. in a first stage, a unified label graph is constructed that includes relevant label semantic information. the unified label graph advantageously unifies structured knowledge represented by a graph with unstructured knowledge given by label descriptions, thereby incorporating more adequate label semantics into text classification. the unified label graph advantageously models relations between labels explicitly, which can help to clarify subtle differences between two labels and identify exceptional sub-concepts under a label. in a second stage, a text classification model predicts an output label that should be applied to an input text using the unified label graph.
