Search Results for author: Mike Zhang

Found 16 papers, 12 papers with code

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

no code implementations8 Feb 2024 Elena Senger, Mike Zhang, Rob van der Goot, Barbara Plank

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis.

Classification

Rethinking Skill Extraction in the Job Market Domain using Large Language Models

no code implementations6 Feb 2024 Khanh Cao Nguyen, Mike Zhang, Syrielle Montariol, Antoine Bosselut

Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes.

Few-Shot Learning In-Context Learning

JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching

1 code implementation5 Feb 2024 Antoine Magron, Anna Dai, Mike Zhang, Syrielle Montariol, Antoine Bosselut

Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations.

Benchmarking Sentence

Entity Linking in the Job Market Domain

1 code implementation31 Jan 2024 Mike Zhang, Rob van der Goot, Barbara Plank

In this work, we are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014).

Entity Linking

NNOSE: Nearest Neighbor Occupational Skill Extraction

1 code implementation30 Jan 2024 Mike Zhang, Rob van der Goot, Min-Yen Kan, Barbara Plank

The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text.

Retrieval

ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain

1 code implementation20 May 2023 Mike Zhang, Rob van der Goot, Barbara Plank

The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification.

De-identification Masked Language Modeling +1

Evidence > Intuition: Transferability Estimation for Encoder Selection

1 code implementation20 Oct 2022 Elisa Bassignana, Max Müller-Eberstein, Mike Zhang, Barbara Plank

With the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori - as fine-tuning the entire space of available LMs is computationally prohibitive and unsustainable.

Structured Prediction

Skill Extraction from Job Postings using Weak Supervision

1 code implementation16 Sep 2022 Mike Zhang, Kristian Nørgaard Jensen, Rob van der Goot, Barbara Plank

Aggregated data obtained from job postings provide powerful insights into labor market demands, and emerging skills, and aid job matching.

Experimental Standards for Deep Learning in Natural Language Processing Research

1 code implementation13 Apr 2022 Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.

Cartography Active Learning

2 code implementations Findings (EMNLP) 2021 Mike Zhang, Barbara Plank

We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling.

Active Learning text-classification +1

The Effect of Translationese in Machine Translation Test Sets

1 code implementation WS 2019 Mike Zhang, Antonio Toral

The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data.

Machine Translation Translation

Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate

no code implementations SEMEVAL 2019 Mike Zhang, Roy David, Leon Graumans, Gerben Timmerman

The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish.

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