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    <titleInfo>
        <title>A machine-learning based model to identify PhD-level skills in job ads</title>
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    <name type="personal">
        <namePart type="given">Li’An</namePart>
        <namePart type="family">Chen</namePart>
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        <namePart type="given">Inger</namePart>
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    <name type="personal">
        <namePart type="given">Hanna</namePart>
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            <title>Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association</title>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>Around 60% of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to more closely align the PhD curriculum with the needs of industry, but an evidence base is lacking to inform these changes. We need to find better ways to understand what industry employers really want from doctoral graduates. One good source of data is job advertisements where employers provide a ‘wish list’ of skills and expertise. In this paper, a machine learning-natural language processing (ML-NLP) based approach was used to explore and extract skill requirements from research intensive job advertisements, suitable for PhD graduates. The model developed for detecting skill requirements in job ads was driven by SVM. The experiment results showed that ML-NLP approach had the potential to replicate manual efforts in understanding job requirements of PhD graduates. Our model offers a new perspective to look at PhD-level job skill requirements.</abstract>
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        <url>https://aclanthology.org/2020.alta-1.8</url>
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        <date>2020-12</date>
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            <start>72</start>
            <end>80</end>
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%0 Conference Proceedings
%T A machine-learning based model to identify PhD-level skills in job ads
%A Chen, Li’An
%A Mewburn, Inger
%A Suonimen, Hanna
%Y Kim, Maria
%Y Beck, Daniel
%Y Mistica, Meladel
%S Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
%D 2020
%8 December
%I Australasian Language Technology Association
%C Virtual Workshop
%F chen-etal-2020-machine
%X Around 60% of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to more closely align the PhD curriculum with the needs of industry, but an evidence base is lacking to inform these changes. We need to find better ways to understand what industry employers really want from doctoral graduates. One good source of data is job advertisements where employers provide a ‘wish list’ of skills and expertise. In this paper, a machine learning-natural language processing (ML-NLP) based approach was used to explore and extract skill requirements from research intensive job advertisements, suitable for PhD graduates. The model developed for detecting skill requirements in job ads was driven by SVM. The experiment results showed that ML-NLP approach had the potential to replicate manual efforts in understanding job requirements of PhD graduates. Our model offers a new perspective to look at PhD-level job skill requirements.
%U https://aclanthology.org/2020.alta-1.8
%P 72-80

Markdown (Informal)

[A machine-learning based model to identify PhD-level skills in job ads](https://aclanthology.org/2020.alta-1.8) (Chen et al., ALTA 2020)

• A machine-learning based model to identify PhD-level skills in job ads (Chen et al., ALTA 2020)

ACL

• Li’An Chen, Inger Mewburn, and Hanna Suonimen. 2020. A machine-learning based model to identify PhD-level skills in job ads. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 72–80, Virtual Workshop. Australasian Language Technology Association.