English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Occupational data mining and analysis is an important task in understandingtoday's industry and job market. Various machine learning techniques areproposed and gradually deployed to improve companies' operations for upstreamtasks, such as employee churn prediction, career trajectory modelling andautomated interview. Job titles analysis and embedding, as the fundamentalbuilding blocks, are crucial upstream tasks to address these occupational datamining and analysis problems. In this work, we present the Industrial andProfessional Occupations Dataset (IPOD), which consists of over 190,000 jobtitles crawled from over 56,000 profiles from Linkedin. We also illustrate theusefulness of IPOD by addressing two challenging upstream tasks, including: (i)proposing Title2vec, a contextual job title vector representation using abidirectional Language Model (biLM) approach; and (ii) addressing the importantoccupational Named Entity Recognition problem using Conditional Random Fields(CRF) and bidirectional Long Short-Term Memory with CRF (LSTM-CRF). Both CRFand LSTM-CRF outperform human and baselines in both exact-match accuracy and F1scores. The dataset and pre-trained embeddings are available athttps://www.github.com/junhua/ipod.

IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and Analysis