Google llc (20240111999). SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS simplified abstract
Contents
- 1 SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
Organization Name
Inventor(s)
Amir Reuven Feder of New York NY (US)
Fan Zhang of Cupertino CA (US)
Ayelet Benjamini of Kfar Saba (IL)
SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111999 titled 'SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
Simplified Explanation
The patent application describes a multi-task neural network system for processing clinical notes.
- Shared neural network processes text spans to generate text segment embeddings.
- Segmentation neural network determines if text segments are section titles.
- Section type classification neural network classifies text segments into section types.
Potential Applications
This technology could be applied in various fields such as healthcare, legal, and academic research where text segmentation and classification are required.
Problems Solved
This technology solves the problem of efficiently processing and categorizing text segments in large documents, such as clinical notes, which can be time-consuming and error-prone when done manually.
Benefits
The benefits of this technology include increased efficiency in analyzing text data, improved accuracy in categorizing text segments, and the ability to handle large volumes of text data in a systematic manner.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of software tools for healthcare professionals to streamline the analysis of clinical notes and improve patient care outcomes.
Possible Prior Art
One possible prior art for this technology could be existing text processing systems that use neural networks for segmentation and classification tasks in various domains.
Unanswered Questions
How does this technology compare to traditional text processing methods?
This article does not provide a direct comparison between this technology and traditional text processing methods.
What are the limitations of this multi-task neural network system?
The article does not address any potential limitations or challenges that may arise when implementing this multi-task neural network system.
Original Abstract Submitted
a multi-task neural network system is described. the system includes a shared neural network configured to receive as input a text span from a clinical note, and for each of one or more text segments in the text span, processing the text segment to generate a set of text segment embeddings. the system further includes a segmentation neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to determine whether the text segment is a section title or not. the system further includes a section type classification neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to classify the text segment into a section type of a plurality of section types.
