Relation Extraction with Deep Neural Language Models

PI: Marco Kuhlmann (LiU). Collaborators: Pierre Nugues (LU); iMatrics AB (Linköping).

The field of natural language processing (NLP) has seen major progress during the last few years with the development of deep neural language models, which learn tasks such as question answering, machine translation, and text summarization without any explicit supervision. This project will apply these models to the task of extracting semantic relations between named entities from raw text. Our main goal is to design, implement, and evaluate an end-to-end system for relation extraction based on deep neural language models. Because training these models from scratch is extremely resource-intensive, we are specifically interested in developing methods for maximizing the performance that can be obtained by fine-tuning pre-trained models, and in particular models for smaller languages such as Swedish. Main topic area Focus Theme 5, ‘Intelligent assistants and tools’.

Project number: A15