Zero-shot Entity Extraction via Bi-level Prompting
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Solution Overview
Problem
Existing entity extraction techniques face challenges in generalizing to new documents with varying schemas, requiring extensive annotation and specialized training, which is costly and not scalable, and struggle to automate data entry from form-like documents effectively.
Innovation Solution
A zero-shot form entity query framework using a bi-level prompting mechanism that encodes document schema and entities into queries for an entity extraction model, allowing for pre-training on large-scale form-like web pages and fine-tuning with human-annotated documents to generalize across different document types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing entity extraction techniques are used, then entities can be extracted from documents, but the system requires extensive annotation and specialized training for each new document schema, which is costly and not scalable
Solution Approach 1:
The patent segments the entity extraction task into two independent components: (1) schema information extraction that identifies the structure and fields of a document type, and (2) entity extraction that retrieves values for those fields. This segmentation allows the system to handle new document schemas without retraining, as the schema extraction component can adapt to new structures independently.
Solution Approach 2:
The patent introduces schema information as an intermediary between the document and the entity extraction process. The schema acts as a mediator that translates new document structures into a standardized format, enabling the core entity extraction model to operate without retraining. The schema information includes field names, data types, and validation rules that guide the extraction process.
2Measurement precision
If specialized training is performed for each document type, then extraction accuracy improves, but the cost and time required for annotation increases significantly
Solution Approach 1:
The patent performs preliminary schema extraction from unannotated documents to create a structured representation of the document type. This preliminary action prepares the system for entity extraction without requiring full annotation of training documents. The schema extraction can be performed automatically using pattern recognition and linguistic analysis.
Solution Approach 2:
The system enables self-service entity extraction by automatically generating schema information from the document structure and using that schema to guide extraction. The model serves itself by inferring schema from examples and using the inferred schema to extract entities, reducing reliance on manually annotated training data.
3Adaptability or versatility
If the system is trained on large amounts of annotated data, then it generalizes better to unseen document types, but the annotation cost becomes prohibitive
Solution Approach 1:
The patent creates a universal entity extraction framework that can handle multiple document types through a single model. The schema extraction component serves multiple functions: it identifies document structure, defines extraction fields, and validates extracted entities. This multi-functionality eliminates the need for separate trained models for each document type.
Solution Approach 2:
The patent changes the parameter space from annotated entity examples to unannotated schema structures. Instead of learning from parameters like entity boundaries and labels in training data, the system learns from document structural parameters such as field names, sections, and hierarchical relationships that can be extracted without annotation.
Data Source
AI summary
A method for extracting entities comprises obtaining a document that includes a series of textual fields that includes a plurality of entities. Each entity represents information associated with a predefined category. The method includes generating, using the document, a series of tokens representing the series of textual fields. The method includes generating an entity prompt that includes the series of tokens and one of the plurality of entities and generating a schema prompt that includes a schema associated with the document. The method includes generating a model query that includes the entity prompt and the schema prompt and determining, using an entity extraction model and the model query, a location of the one of the plurality of entities among the series of tokens. The method includes extracting, from the document, the one of the plurality of entities using the location of the one of the plurality of entities.