JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning

• URL: http://arxiv.org/abs/2202.10739v2
• Date: Mon, 23 Oct 2023 21:30:42 GMT
• Title: JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning
• Authors: Michiharu Yamashita, Jia Tracy Shen, Thanh Tran, Hamoon Ekhtiari, Dongwon Lee
• Abstract summary: In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks. Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles.
• Score: 5.5324736938802435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users' career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. However, solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles in real-world applications. To this end, we propose a novel solution, named JAMES, that constructs three unique embeddings (i.e., graph, contextual, and syntactic) of a target job title to effectively capture its various traits. We further propose a multi-aspect co-attention mechanism to attentively combine these embeddings, and employ neural logical reasoning representations to collaboratively estimate similarities between messy job titles and normalized job titles in a reasoning space. To evaluate JAMES, we conduct comprehensive experiments against ten competing models on a large-scale real-world dataset with over 350,000 job titles. Our experimental results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively.

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