information extraction and text analytics

Dissertation topics suggested by Andreas VlachidisI welcome dissertation proposals that relate to information extraction and text analyticswith focus on the Humanities and Social science domains. If you are interested in any of thefollowing topics, please email me to arrange a meeting. The topics will be allocated on afirst-come-first-served basis. Note that students who would like to work on any topicssuggested by an member of staff should not expect from that member of staff or theirsupervisor any more guidance or help than they would get if they choose their own topic. Itis the student’s responsibility to further develop or narrow down the topic and do therelevant research.1. Negation Detection approaches in the humanities domainTopic area: Information Extraction, Semantic AnnotationSuitable for: For students interested in Information Extraction and Semantic Annotation ofhumanities text.Contact: Dr. Andreas Vlachidis , The techniques and approaches that are employed to address the issue ofnegation within Natural Language Processing vary and cover a wide spectrum of ruledbased, machine learning and lately, deep learning applications. However, the focus so farhas been on the biomedicine domain and there is little evidence of research aimed at thestudy of negation detection in the domain of humanities. A dissertation in this area mightexplore areas of application of negation detection in the domain of Humanities and proposepathways for the automatic detection of negated expressions in unstructured text. Otherresearch pathways to the Negation Detection problem might focus on the ontologicalmodelling of negated facts where the focus shifts from positive to negative assertions, andcomparison and benchmarking of the transferability qualities of existing tools to thehumanities domain.2. Text mining and Critical Discourse Analysis on cultural and social contextsTopic area: Text Analysis, Corpus linguistics, Corpus-Based quantitative methodsSuitable for: For students with interest in computer-linguistic and quantitative methodsContact: Dr. Andreas Vlachidis , Over the past few years, text mining has started to catch on the domains ofsocial sciences, anthropology, education and sociology. Using automated methods ofcorpus linguistics and critical discourse analysis we can identify the presence of features ofinterest over large document collections and investigate social and cultural phenomenathrough quantitative analysis methods. A dissertation on this area might examine thepolitical economy of AI technologies in educational or cultural contexts which could lead toforms or exclusion and discrimination; or the way ‘immigrant workers’ and ‘asylum seekers ’are represented in news reporting; or similar social and cultural phenomena that can beinvestigated using CDA quantitative methods.3. Named Entity Recognition and Linking of Ancient and Historic PlacesTopic area: Information Extraction, Named Entity Recognition and Linking, Linked OpenDataSuitable for: For students interested in Named Entity Recognition and Semantic WebTechnologiesContact: Dr. Andreas Vlachidis , Named Entity Recognition (NER), is a particular Natural Language Processing(NLP) task aimed at the recognition and classification of units of information to predefinedcategories, such as names of person, location, organisation, etc. Several NER Linkingapplications have become available recently that link textual mentions of entities tocorresponding LoD unique references that originate from general purpose knowledge basessuch as Wikipedia. However, use of domain specific knowledge bases for a finer and moreaccurate linking of entities to corresponding reference remains an unexplored area. Adissertation in this area might explore the integration of the domain focused place nameresources such Pleiades ( in the process of Named EntityRecognition and Linking.

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