‪Irena Spasić

Irena Spasic

Terms are linguistic signifiers of domain-specific concepts. Automated recognition of multi-word terms (MWT) in free text is a sequence labelling problem, which is commonly addressed using supervised machine learning methods. Their need for manual annotation of training data makes it difficult to port such methods across domains. FlexiTerm, on the other hand, is a fully unsupervised method for MWT recognition from domain-specific corpora. Originally implemented in Java as a proof of concept, it did not scale well, thus offering little practical value in the context of big data. In this paper, we describe its re-implementation in Python and compare the performance of these two implementations. The results demonstrated major improvements in terms of efficiency, which allow FlexiTerm to transition from the proof of concept to the production-grade application.

Dawn Knight, Steve Morris, Tess Fitzpatrick et al.

This report provides an overview of the CorCenCC project and the online corpus resource that was developed as a result of work on the project. The report lays out the theoretical underpinnings of the research, demonstrating how the project has built on and extended this theory. We also raise and discuss some of the key operational questions that arose during the course of the project, outlining the ways in which they were answered, the impact of these decisions on the resource that has been produced and the longer-term contribution they will make to practices in corpus-building. Finally, we discuss some of the applications and the utility of the work, outlining the impact that CorCenCC is set to have on a range of different individuals and user groups.