Talent-Screening Question Generation via Ngram Filtering
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Solution Overview
Problem
Existing talent-screening question recommendation systems are not able to automatically adapt to evolving job market trends, often resulting in irrelevant or false positive recommendations, and require significant human labor for manual labeling.
Innovation Solution
An algorithm that analyzes job postings to discover and extract key phrases indicating trending job requirements, generating new talent-screening questions by filtering ngrams, removing stop and requirement words, and matching seed phrases to job descriptions to recommend relevant questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training set is used for talent-screening question recommendation, then recommendation accuracy is improved, but human labor requirement increases significantly
Solution Approach 1:
The system performs automatic text processing, ngram generation, and seed phrase identification without human intervention. The algorithm autonomously analyzes job descriptions, generates candidate screening questions, and ranks them based on relevance scores, eliminating the need for manual labeling of training data while maintaining recommendation quality
Solution Approach 2:
The patent replaces manual human labor with an automated computational system that uses text processing algorithms, ngram analysis, and scoring mechanisms to generate talent-screening questions. This mechanical substitution eliminates the need for human annotators while preserving the ability to produce accurate recommendations
2Speed
If word searches are used to identify job requirements, then processing speed is improved, but false positives increase
Solution Approach 1:
The patent segments the job description text into ngrams (sequences of words) and further processes them to identify seed phrases. This segmentation allows the system to analyze specific word combinations rather than relying on simple keyword matching, thereby reducing false positives while maintaining processing efficiency
Solution Approach 2:
The system applies different processing treatments to different parts of the text. It identifies seed phrases with specific characteristics (removing stop words, requiring minimum length) and applies context-aware scoring that considers the local textual environment, thereby improving reliability without sacrificing speed
3Device complexity
If predefined talent-screening questions are used, then system complexity is reduced, but adaptability to evolving job market trends decreases
Solution Approach 1:
The system dynamically generates talent-screening questions based on the specific job description provided. Rather than using a static predefined list, the algorithm processes the input text to extract relevant seed phrases and generates customized screening questions that adapt to current job market requirements and specific role needs
Solution Approach 2:
The system changes the parameters of the screening questions based on the job description analysis. It adjusts the content, focus, and specificity of questions according to the extracted seed phrases and their relevance scores, allowing the system to adapt to evolving job market trends while maintaining a relatively simple overall architecture
Data Source
AI summary
Methods, systems, and computer programs are presented for automatically generating phrase-based talent-screening questions. One method includes analyzing job descriptions to generate ngrams. Each ngram comprises one or more words. Further, the method includes identifying, from the ngrams, an ngram set comprising a predetermined number of bigrams and trigrams according to frequency of appearance in the job descriptions. The method further includes removing, from the ngram set, bigrams and trigrams comprising one or more of stop words, negation words, or requirement words, to obtain first seed phrases. The first seed phrases are filtered based on a frequency of appearance of the seed phrase in the job descriptions to obtain second seed phrases. Further, the second seed phrases are added to the first seed phrases to obtain third seed phrases. Each seed phrase is a sequence of one or more words that is associated with a category of talent-screening questions.