**Company: UCLouvain Saint-Louis Bruxelles Job Description Summary: The Centre for English Corpus Linguistics (CECL, UCLouvain, Belgium) is offering a PhD fellowship in linguistics starting in October 2024. The main objective of the PhD project is to explore the construct of ‘phraseological complexity’ using the method of Comparative Judgment. The project will test the reliability of human judgments of phraseological complexity and compare them with automated measures. Candidates must have a Master’s degree in Linguistics or Natural Language Processing, excellent analytical skills, knowledge of corpus linguistic techniques, and proficiency in English. The fellowship is for one year, renewable up to four years, with a monthly grant starting at around EUR 2400 net. The candidate must reside in Belgium and be willing to travel for international conferences. Applicants from outside the EU must obtain necessary visas with UCLouvain’s assistance. Job Description: The Centre for English Corpus Linguistics (CECL, UCLouvain, Belgium) has an opening for a PhD fellowship in linguistics. * Full-time (100%) PhD fellowship for one year, renewable (max. 4 years) * Start date: October 2024 The main objective of the PhD project will be to use the method of Comparative Judgment to explore whether the theoretical construct of ‘phraseological complexity’ (Paquot, 2019) is reflective of collective understandings of the linguistic phenomena we wish to measure with such a construct. The project will centre around two main objectives: (1) test empirically whether reliable human judgments of phraseological complexity are attainable (both at the level of multi-word units and at the text level); (2) explore the degree to which various automated measures of phraseological complexity align with human judgments of phraseological complexity, which is to be understood as our ‘gold standard’ to assess construct validity. For more information about phraseological complexity, the method of Comparative Judgment and the use of human judgments to explore the multidimensionality of our research constructs, see: * Kyle, K., Crossley, S. A., & Jarvis, S. (2021). Assessing the Validity of Lexical Diversity Indices Using Direct Judgements. Language Assessment Quarterly, 18(2), 154‑170. https://doi.org/10.1080/15434303.2020.1844205 * Paquot, M. (2019). The phraseological dimension in interlanguage complexity research. Second Language Research, 35(1), 121–145. https://doi.org/10.1177/0267658317694221 * Paquot, M., Rubin, R. & Vandeweerd, N. (2022). Crowdsourced Adaptive Comparative Judgment: A community-based solution for proficiency rating. Language Learning 72(3), 853-885. https://onlinelibrary.wiley.com/doi/10.1111/lang.12498
Company:UCLouvain
Qualifications:The candidate will meet the following qualifications: * Master degree in (Applied) Linguistics or Natural Language Processing * Excellent record of BA and MA level study * Excellent oral and written English (minimum level: C1) * Excellent analytic skills * Knowledge of corpus linguistic techniques is a requirement * Knowledge of statistics and R is an asset * Autonomy, sense of teamwork, ability to listen and analyse needs, responsiveness * Willingness to live in Belgium and to travel abroad (to attend international academic conferences)
Specific requirements:This doctoral fellowship is subject to the following conditions: * The contract will initially be for one year, three times renewable, with a total of four years. * The candidate receives a doctoral fellowship grant (starting at approx. EUR 2400 net per month) and full medical insurance. * This position requires residence in Belgium for the duration of the mandate. * Applicants from outside the EU are responsible for obtaining the necessary visa or permits, with the assistance of UCLouvain staff department.
Educational level:Master Degree
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
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Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.
