Deep learning for natural language text and image analysis

This research project focuses on natural language text and image analysis based on statistical and deep learning techniques, for recognizing semantics or meaningful relations from unstructured big data, for better understanding of the data and serving the users.
This project aims to develop a mixed natural language text and image analysis system for an online social network environment, where plenty of articles and comments including both text and images are increasingly produced by its users. Several predefined events and semantic relations useful for the stakeholders will be extracted, identified and recognized against the mixed data by using statistical and deep learning algorithms, and then visualized in an interface as real time report. The key research problems include how to choose suitable language and image processing models, how to design effective statistical and deep learning algorithms and programs for analyzing the unstructured data, and how to make use of higher level semantic information in the analysis. Moreover, some additional information associated with multiple modals, will ideally be jointly modeled in the learning process and further leveraged for analysis. The prototype system of the project should be able to perform effective text and image analysis with expected outputs for the purpose of proof of concept.

Faculty Supervisor:

Dunwei Wen

Student:

HUILIANG LING

Partner:

Discipline:

Computer science

Sector:

University:

Athabasca University

Program:

Globalink