18048900. LABEL INDUCTION simplified abstract (Adobe Inc.)

LABEL INDUCTION

Organization Name

Adobe Inc.

Inventor(s)

Rajiv Bhawanji Jain of Falls Church VA (US)

Michelle Yuan of Montclair NJ (US)

Vlad Ion Morariu of Potomac MD (US)

Ani Nenkova Nenkova of Philadelphia PA (US)

Smitha Bangalore Naresh of Sudbury MA (US)

Nikolaos Barmpalios of Palo Alto CA (US)

Ruchi Deshpande of Belmont CA (US)

Ruiyi Zhang of San Jose CA (US)

Jiuxiang Gu of Baltimore MD (US)

Varun Manjunatha of Newton MA (US)

Nedim Lipka of Campbell CA (US)

Andrew Marc Greene of Newton MA (US)

LABEL INDUCTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18048900 titled 'LABEL INDUCTION

Simplified Explanation: The patent application describes systems and methods for document classification using a neural network trained to identify known classes and an open-set metric to annotate unknown classes.

• Key Features and Innovation:

   - Utilizes a neural network to classify documents into known classes.
   - Selects samples for annotation based on an open-set metric.
   - Trains the neural network to identify unknown classes through annotation.

• Potential Applications:

   - Document categorization in various industries.
   - Information retrieval systems.
   - Automated content tagging for large datasets.

• Problems Solved:

   - Efficient document classification with the ability to identify unknown classes.
   - Reducing manual effort in annotating documents for classification.
   - Improving accuracy and scalability of document classification systems.

• Benefits:

   - Enhanced accuracy in document classification.
   - Increased efficiency in handling large volumes of data.
   - Adaptability to new and evolving document classes.

• Commercial Applications:

   - "Advanced Document Classification System for Enhanced Information Retrieval and Content Tagging"

• Prior Art:

   - No prior art information provided.

• Frequently Updated Research:

   - No information on frequently updated research provided.

Questions about Document Classification Technology: 1. How does the neural network identify unknown classes in document classification? The neural network is trained using an open-set metric to annotate unknown classes in a set of samples, allowing it to learn and recognize these classes based on the annotations.

2. What are the potential challenges in implementing this document classification system in real-world applications? One potential challenge could be the need for extensive training data to accurately identify a wide range of known and unknown classes, as well as the computational resources required for training and inference.

Original Abstract Submitted

Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.