Labelling Topics Using Neural Networks
I am an Applied Scientist at Amazon, Cambridge, UK. Prior to that, I worked as a Research Associate at the Department of Computer Science at UCL and I completed a PhD in NLP at the Department of Computer Science of the University of Sheffield. My main research interests are in Natural Language Processing and Machine Learning. More specifically, I'm interested in applying statistical methods for detecting and interpreting the underlying topics in large volumes of text data. I also develop methods to analyse text and uncover patterns in data to solve problems in other scientific areas such as social and legal science.
Much of the information in large digital libraries is often stored in an unstructured way and is not organised using any automated system. That is usually overwhelming for users in a way that makes it difficult to find specific information or explore such data collections. A particular set of unsupervised statistical methods, namely topic models have been extensively used in Natural Language Processing and Information Retrieval for automatically analysing and organising document collections. Topics generated by topic models are typically presented as a list of terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. In this talk, I will present neural network approaches to labelling topics with text and images showcasing their effectiveness on providing meaningful representations of the topics.