Multiword expression identification using deep learning
University of New Brunswick
Multiword expressions combine words in various ways to produce phrases that have properties that are not predictable from the properties of their individual words or their normal mode of combination. There are many types of multiword expressions including proverbs, named entities, and verb noun combinations. In this thesis, we propose various deep learning models to identify multiword expressions and compare their performance to more traditional machine learning models and current multiword expression identification systems. We show that convolutional neural networks are able to perform better than state-of-the-art with the three hidden layer convolutional neural network performing best. To our knowledge, this is the first work that applies deep learning models for broad multiword expression identification.