Year: 2018
Type: dissertation
Abstract: Learning distributed representations of sentences and analyzing semantic similarity between sentences is one of the essential works in the field of Natural Language Processing. In the domain of legal language, the future of Artificial Intelligence-related legal-tech applications is very promising. This thesis comprises a very detailed investigation of distributional representations of words and sentences, and the related machine learning and deep learning techniques. Then, we proposed an innovative approach, Word2Sent, for measuring the degree of similarity between sentences. The proposed model is completely in an unsupervised manner. Thus, it can be well applied with unlabeled data. An enhancement of the other unsupervised sentence embeddings model, SIF-model, is made by this thesis. Demonstrated by multiple experiments, our proposed model can effectively work with long legal sentences on several textual similarity tasks.
Source: UWSpace (University of Waterloo)
Author: Ying Wang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: University of Waterloo
OpenAlex ID: W2910484384