| KEYWORD |
Deep Learning System to Characterize Scholars using Scientific Papers
keywords MACHINE LEARNING, NATURAL LANGUAGE PROCESSING, SUMMARIZATION
Reference persons MAURIZIO MORISIO
External reference persons Giuseppe Rizzo, ISMB
Research Groups GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG
Thesis type EXPERIMENTAL
Description A critical pain of all organizations is managing competencies of their personnel due to both internal variations of topic characterizations (usually an employee acquires knowledge and evolves his professional competence spectrum) and external towards aligning new trends and market requirements.
In this thesis the undergraduate will develop an intelligent system for compressing scientific papers into a list of topics in a lossy manner using both semantic analysis and deep learning. The system will be able to learn autonomously from a set of scientific papers authored by scholars and be able to characterize an unknown scholar starting from her/his set of scientific articles.
The thesis will be structured as follows:
state-of-the-art critical analysis in the field of document summarization using both semantic analysis and deep learning;
problem formulation: objective function, data structures and resources to be used;
algorithm design and prototyping;
in-lab testing verification with real data and measurement of the goodness of the approach.
The undergraduate will benefit from being immersed in a research environment. It’s a unique setting to get into a research mindset with a strong push for innovation. At the end of the thesis the undergraduate will be familiar with deep learning and semantic analysis and be able to implement an intelligent system. As additional benefit, she/he will use proficiently control version systems, continuous integration systems, remote deploying and monitoring techniques.
Required skills python, java
Deadline 31/01/2019
PROPONI LA TUA CANDIDATURA