Entity Extraction Using Conditional Random Field Context Modeling
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Solution Overview
Problem
Existing methods for entity extraction in electronic documents rely on exact matches or grammatical analysis, failing to account for semantic context, leading to inaccuracies in identifying entities such as proper nouns or names referred to in different contexts.
Innovation Solution
A conditional random field algorithm is used to generate a model that deconstructs text into sentences and tokens, identifying entities based on context, allowing for improved entity extraction by considering neighboring sentences and tolerating ambiguous words, without requiring additional training documents or deep grammar parsing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art solutions use exact match or grammatical analysis to identify entities, then the method is simple and fast, but the accuracy is poor because semantic context is not considered
Solution Approach 1:
The patent introduces a Conditional Random Field (CRF) model as an intermediary layer between the input text and entity extraction. This CRF model processes the sequential context of tokens and sentences, allowing the system to consider semantic relationships without requiring complex manual feature engineering. The CRF acts as a mediator that transforms raw text into context-enriched representations that improve entity identification accuracy.
Solution Approach 2:
The patent changes the parameters of the entity extraction system by transitioning from simple token matching to a probabilistic framework. The CRF model introduces parameters such as transition probabilities between states and emission probabilities for entity labels, allowing the system to dynamically adjust entity extraction based on contextual evidence rather than relying on fixed grammatical rules.
2Measurement precision
If prior art solutions analyze grammar and syntax to infer entities, then additional computational resources are required, but accuracy still suffers because semantic context is not fully utilized
Solution Approach 1:
The patent applies preliminary action by pre-training the CRF model on annotated corpus data before deployment. During this offline training phase, the model learns contextual patterns and semantic relationships from labeled examples. Once trained, the model can be applied to new documents with minimal computational overhead, as the heavy lifting of learning contextual relationships has already been performed during training.
Solution Approach 2:
The patent uses copying by leveraging annotated training data to create a trained CRF model that can be replicated and applied to multiple documents. The learned parameters and patterns from the training corpus are copied into the model structure, allowing the system to generalize contextual understanding across different documents without reprocessing the entire training set each time.
3Measurement precision
If the system considers semantic context of phrases, then entity extraction accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the text into discrete tokens and processing them sequentially through the CRF model. The CRF processes the text token by token, maintaining a hidden state that captures contextual information. This segmented approach allows the model to consider semantic context efficiently by processing local neighborhoods of tokens rather than analyzing the entire document globally at once.
Data Source
AI summary
A method for entity extraction within an electronic document including executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model; the conditional random field algorithm having an input including one or more training text documents; executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model; the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm; applying by a computer processor the conditional random field model to at least one electronic document; wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity; applying by a computer processor the entity extraction model to the at least one electronic document; wherein application of the entity extraction model returns a list of entities; and storing the list of entities on a compute readable medium.