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Mining Arguments from Natural Language Resources - Project ID SOC0015


School of Computing

About the Project

Argument Mining is the automatic identification and extraction of argumentative structure from within real world textual resources such as Internet discourse, legal documents, or newspaper articles. This project will involve a detailed study of the structure of natural language arguments with the aim of devising new and effective computational mining techniques. The successful candidate will be expected to further focus their project, and may choose for example, to focus on the effective application or extension of existing natural language or machine learning techniques applied to the argument mining domain.

Academic qualifications
A first degree (at least a 2.1) ideally in Computer Science or Computational Linguistics with a good fundamental knowledge of natural language processing techniques or machine learning.

English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available here https://www.napier.ac.uk/research-and-innovation/research-degrees/application-process

Essential attributes:
• Experience of fundamental Natural Language Processing or Machine Learning techniques
• Competent in applying NLP toolkits, such as NLTK or Spacy, or ML toolkits such as Scikit-Learn or Tensorflow
• Knowledge of Argumentation Theory
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:
Understanding of topics in machine learning, computational argumentation, and defeasible reasoning would be advantageous.

When applying for this position please quote Project ID SOC0015

Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community https://www.napier.ac.uk/about-us/university-governance/equality-and-diversity-information

Funding Notes

This is an unfunded position

References

Lippi & Torroni (2016) “Argumentation Mining: State of the Art and Emerging Trends“ is a good overview

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