Multilingual Job Title Embedding Mapping
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Solution Overview
Problem
Existing technologies face challenges in accurately mapping raw job titles from online job postings to corresponding entities in a title taxonomy, especially due to noise in the raw text and the presence of job titles in low resource languages without explicit entries in the taxonomy.
Innovation Solution
The technique involves using deep learning to align a multilingual word embedding space for job titles with an entity embedding space for a title taxonomy. This is achieved by projecting multilingual word embeddings into the entity embedding space using a neural network, followed by a nearest neighbor search to identify the closest matching entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text matching methods are used to map job titles to taxonomy entities, then the system is simple to implement, but the accuracy deteriorates due to noise in raw text and lack of explicit entries for low resource languages
Solution Approach 1:
The patent introduces multilingual word embeddings as an intermediary layer between raw job title text and the title taxonomy. These embeddings transform text from multiple languages into a unified vector space, enabling accurate mapping even for low resource languages without explicit taxonomy entries. The neural network then maps these embeddings to the entity embedding space, serving as a mediator that bridges the gap between noisy raw text and standardized taxonomy entities.
Solution Approach 2:
The patent replaces traditional mechanical text matching methods (string comparison, keyword matching) with a neural network-based embedding system. This substitution enables the system to handle noise in raw text and process low resource languages by leveraging semantic representations rather than exact string matches, significantly improving mapping accuracy.
2Measurement precision
If multilingual word embeddings are projected into entity embedding space using neural network, then the mapping accuracy for low resource languages improves, but the computational resources and processing time increase
Solution Approach 1:
The patent pre-computes and stores multilingual word embeddings for a large vocabulary of job titles before runtime processing. During actual mapping operations, the system only needs to perform neural network inference on the embedding vectors rather than processing raw text from scratch, significantly reducing computational energy consumption while maintaining high accuracy for low resource languages.
3Measurement precision
If exact string matching is used for job title standardization, then the processing speed is fast, but the accuracy deteriorates due to noise and variations in raw job title text
Solution Approach 1:
The patent transforms job title text from its original string form into vector embeddings, changing the parameter space from discrete text tokens to continuous vector representations. This transformation enables the system to capture semantic meaning and handle text variations, noise, and translations, significantly improving standardization accuracy while the efficient neural network inference maintains acceptable processing speed.
Data Source
AI summary
Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.