Structural Data Extraction with Neural Entity Classification
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Solution Overview
Problem
Existing online booking systems face challenges in accurately extracting and classifying structured data from unstructured text streams, which affects the performance and user experience in listing platforms for temporary stays.
Innovation Solution
A Listing Attribute Extraction Platform (LAEP) system utilizing neural network models for Named Entity Recognition (NER), Entity Mapping (EM), and Entity Scoring (ES) to automate the extraction of structured data from unstructured text, including amenities, facilities, and location features, using transformer and BERT models for contextual classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data extraction methods are implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces manual mechanical data extraction processes with automated neural network-based systems. The NER models automatically identify and extract entities from unstructured text, substituting human analysts with intelligent algorithms that process data at scale while maintaining consistent accuracy through trained patterns rather than manual judgment.
Solution Approach 2:
The system enables self-service data extraction where the neural networks autonomously perform entity recognition, classification, and attribute extraction without human intervention. The models self-adjust through training on labeled data and automatically adapt to different text formats and domains, providing continuous automated extraction service that improves both productivity and precision through iterative learning.
2Loss of information
If comprehensive data extraction is performed, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The patent segments the complex data extraction task into distinct functional components: Named Entity Recognition for identifying entities, Entity Mapping for classifying entities into categories, and Attribute Extraction for pulling specific properties. This segmentation allows each component to specialize in one aspect of information extraction, reducing overall system complexity while comprehensively capturing all relevant information through coordinated operation of the segments.
Solution Approach 2:
The patent introduces intermediary layers between raw text and final structured data, including tokenization modules that break text into manageable units, embedding layers that transform text into numerical representations, and attention mechanisms that selectively focus on relevant portions. These intermediaries simplify the extraction process by progressively transforming unstructured text into organized intermediate representations before final attribute extraction.
Data Source
AI summary
Systems and methods are provided. In one example, a method includes receiving unstructured text documents associated with a plurality of listings hosted on a listing network platform. For each listing in the plurality of listings, the method further includes analyzing the unstructured text documents to detect named entities corresponding to one or more entity types using a first trained neural network model. The method additionally includes mapping the detected named entities to a standardized taxonomy of listing attributes by using a mapping space. The method also includes storing, in a searchable knowledge base, the mapped listing attributes to refer to corresponding listings in the plurality of listings, and providing, via the one or more processors, for a search facility to search the searchable knowledge base for one or more listings in the plurality of listings having a listing attribute.