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About Job Skills Thesis
This paper compares unsupervised and supervised machine learning techniques for job skills extraction from text documents. The author trains an LSTM model combined with word embeddings on job postings to identify skills. Topic modeling and word2vec were also tested but produced noisy results. A labeled dataset of noun phrases was created and used to train a word embeddings classifier and an LSTM + word embeddings model, which achieved better performance at skill extraction compared to unsupervised methods. The approach aims to extract both existing and new emerging skills from job descriptions.
Detailed Information
| Author: | ['Candy Somar'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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