Jun Zhu


2022

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Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning
Jun Zhu | Celine Hudelot
Findings of the Association for Computational Linguistics: NAACL 2022

Works on learning job title representation are mainly based on Job-Transition Graph, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct Job-Transition-Tag Graph, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the Job-Transition-Tag Graph. Experiments on two datasets show the interest of our approach.

2016

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Generative Topic Embedding: a Continuous Representation of Documents
Shaohua Li | Tat-Seng Chua | Jun Zhu | Chunyan Miao
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Discriminative Deep Random Walk for Network Classification
Juzheng Li | Jun Zhu | Bo Zhang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields
Jingwei Zhuo | Yong Cao | Jun Zhu | Bo Zhang | Zaiqing Nie
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Shaohua Li | Jun Zhu | Chunyan Miao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2013

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Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Jun Zhu | Xun Zheng | Bo Zhang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)