PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

Scientific Content Generation Using Semantic Analysis and Deep Learning

Parole chiave MACHINE LEARNING, NATURAL LANGUAGE PROCESSING

Riferimenti MAURIZIO MORISIO

Riferimenti esterni Giuseppe Rizzo, ISMB

Gruppi di ricerca GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG

Tipo tesi EXPERIMENTAL

Descrizione Automated assistants are now more than ever taking place in our daily life. Assistants are thus asked to generate content according to user’ inputs and contextual objectives.

Let take the case of a scientist in his daily task of performing experiments, filling tables and reporting findings. Lots of his time is spent in transcribing findings that have been already elaborated and encoded in tables. The advancements achieved in artificial intelligence support scenarios of co-operation between an artificial intelligence-based assistant and a scientist when writing technical reports. The objective of this thesis will be thus researching and prototyping an intelligent system able to write science starting from tables.

In this thesis the undergraduate will develop an AI-based system for writing scientific papers using both semantic analysis and deep learning. The system will be able to learn autonomously from pairs of tables and papers created as gold examples and generate from a newer table a report.

The thesis will be structured as follows:
state-of-the-art critical analysis in the field of document generation 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.

Conoscenze richieste python, java


Scadenza validita proposta 31/01/2019      PROPONI LA TUA CANDIDATURA