Stacked CRF Entity Extraction for Resume Parsing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing entity extraction systems from resumes, such as those using Conditional Random Fields (CRFs), struggle with accurately extracting information due to variations in document format, section order, and professional domain-specific vocabulary, often missing entities that are far apart or not consistently formatted.
Innovation Solution
A method and system that employs machine learning for contextual sequence learning and correction, involving a second pass of entity extraction that clusters entities based on location and learns patterns from complete clusters to identify and extract new entities missed in the first pass, using a combination of entity clustering, cluster completeness evaluation, and pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear-chain CRF is used for entity extraction, then the extraction process is simple and fast, but entities that are far apart are missed
Solution Approach 1:
The patent transitions from linear-chain CRF to stacked CRF architecture, adding a vertical dimension to the extraction process. The first CRF processes entities at the word level, while the second CRF processes entities at the phrase level, creating a multi-layered extraction system that captures both local and distant entities effectively
2Loss of information
If higher-order models like Comb-chain CRF or Skyp-chain CRF are used to detect long-range dependencies, then entities far apart can be detected, but boundary detection causes some entities to be missed
Solution Approach 1:
The patent segments the entity extraction task into two distinct phases using stacked CRF: the first CRF handles word-level entity boundaries with high precision, while the second CRF handles phrase-level entities that may span multiple words. This segmentation allows each CRF to specialize in different aspects of entity detection, avoiding the boundary detection problems of higher-order models
3Device complexity
If rule-based or single-pass machine learning methods are used, then the extraction process is straightforward, but variations in document format and section order reduce extraction accuracy
Solution Approach 1:
The patent implements a dynamic, two-pass extraction system where the first CRF adapts to word-level variations in the document, and the second CRF adapts to phrase-level variations. This dynamic approach allows the system to handle different document formats, section orders, and professional domain vocabularies by adjusting its extraction strategy at different levels of the stacked architecture
Data Source
AI summary
A method for extracting entities from a text document includes, for at least a section of a text document, providing a first set of entities extracted from the at least a section, clustering at least a subset of the extracted entities in the first set into clusters, based on locations of the entities in the document. Complete ones of the clusters of entities are identified. Patterns for extracting new entities are learned based on the complete clusters. New entities are extracted from incomplete clusters based on the learned patterns.