Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.

Attention-based Model for Evaluating the Complexity of Sentences in English Language

The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in tw…

Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.

A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View

Abstract The goal of a text simplification system (TS) is to create a new text suited to the characteristics of a reader, with the final goal of making it more understandable.The building of an Automatic Text Simplification System (ATS) cannot be separated from a correct evaluation of the text complexity. In fact the ATS must be capable of understanding if a text should be simplified for the target reader or not. In a previous work we have presented a model capable of classifying Italian sentences based on their complexity level. Our model is a Long Short Term Memory (LSTM) Neural Network capable of learning the features of easy-to-read and complex-to-read sentences autonomously from a anno…

A Novel Approach for Supporting Italian Satire Detection Through Deep Learning

Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processing, and social multimedia sentiment analysis. In particular, as satirical texts include figurative communication for expressing ideas/opinions concerning people, sentiment analysis systems may be negatively affected; therefore, satire should be adequately addressed to avoid such systems’ performance degradation. This paper tackles automatic satire…

A sentence based system for measuring syntax complexity using a recurrent deep neural network

In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification.

A social humanoid robot as a playfellow for vocabulary enhancement

We introduce a system that exploits a Pepper humanoid robot acting as a playfellow in a word-play game. The robot can play a portmanteau game by directly interacting with children, and it exploits a conversation engine, a portmanteau creation engine, and a definition engine. The humanoid can play the role of either an answerer or a generator of new words.

Towards A Deep-Learning-Based Methodology for Supporting Satire Detection (S)

Multi-class Text Complexity Evaluation via Deep Neural Networks

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…

A Case Study for the Design and Implementation of Immersive Experiences in Support of Sicilian Cultural Heritage

Virtual Reality (VR) is a robust tool for sponsoring Cultural Heritage sites. It enables immersive experiences in which the user can enjoy the cultural assets virtually, behaving as he/she would do in the real world. The covid-19 pandemic has shed light on the importance of using VR in cultural heritage, showing advantages for the users that can visit the site safely through specific devices. In this work, we present the processes that lead to the creation of an immersive app that makes explorable a famous cultural asset in Sicily, the church of SS. Crocifisso al Calvario. The application creation process will be described in each of its parts, beginning from the digital acquisition of the …

DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

Abstract Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named De…

Machine Learning Models for Measuring Syntax Complexity of English Text

In this paper we propose a methodology to assess the syntax complexity of a sentence representing it as sequence of parts-of-speech and comparing Recurrent Neural Networks and Support Vector Machine. We have carried out experiments in English language which are compared with previous results obtained for the Italian one.

WORDY: a Semi-automatic Methodology aimed at the Creation of Neologisms based on a Semantic Network and Blending Devices

In this paper, we propose a semi-automatic tool, named WORDY, that implements a methodology aimed at speeding-up the pro- cess of creation of neologisms. The approach exploits a semantic network, which is explored through the spreading activation methodology and ex- ploits three blending linguistic techniques together with a proper ranking function in order to support companies in the creation of neologisms ca- pable of evoking semantic meaningful associations to customers.

Exploring learning analytics on YouTube: a tool to support students interactions analysis

YouTube is a free online video-sharing platform that is often used by students for their learning activities. The interactions of the students when using the platform to shape new concepts, are worth to be investigated to better understand and to optimize the learning opportunities that take place in this platform. In this paper, we investigate which types of data are relevant to analyse the interactions of students with content on YouTube, and we introduce a new tool that emulates students’ interactions with the platform in order to provide data to be used in supporting Learning Analytics approaches. Our preliminary study inspects the tool effectiveness in data collection and analyses the …

Portmanteau Word-Play for Vocabulary Enhancement with Humanoid Robot Support

Word-play is as powerful learning and motivation tool often used by educators for teaching the ability of reading, which is a complex activity. In this paper, we introduce a system that exploits a Pepper humanoid robot acting as a playfellow in a word-play game based on portmanteau words. The robot shows the ability to play with children using a conversation engine, a portmanteau creation engine, and a definition engine. In this manner, Pepper can integrate itself within a group of kids, and it can support a teacher in her activities. The humanoid can be involved in a word-based round-game in which it can play the role of either answerer or generator of new words.

A Pipeline for the Implementation of Immersive Experience in Cultural Heritage Sites in Sicily

Modern digital technologies allow potentially to explore Cultural Heritage sites in immersive virtual environments. This is surely an advantage for the users that can better experiment and understand a specific site, also before a real visit. This specific approach has gained increasing attention during the extreme conditions of the recent COVID-19 pandemic. In this work, we present the processes that lead to the implementation of an immersive app for different kinds of low and high cost devices, which have been attained in the context of the 3dLab-Sicilia project. 3dLab-Sicilia’s main objective is to sponsor the creation, development, and validation of a sustainable infrastructure that int…

A recurrent deep neural network model to measure sentence complexity for the Italian Language

Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS…

Deep neural attention-based model for the evaluation of italian sentences complexity

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.

Enriching Didactic Similarity Measures of Concept Maps by a Deep Learning Based Approach

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students' assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher's assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appea…

A Controllable Text Simplification System for the Italian Language

Text simplification is a non-trivial task that aims at reducing the linguistic complexity of written texts. Researchers have studied the problem by proposing new methodologies for addressing the English language, but other languages, like the Italian one, are almost unexplored. In this paper, we give a contribution to the enhancement of the Automated Text Simplification research by presenting a deep learning-based system, inspired by a state of the art system for the English language, capable of simplifying Italian texts. The system has been trained and tested by leveraging the Italian version of Newsela; it has shown promising results by achieving a SARI value of 30.17.

Intelligent Knowledge Understanding from Students Questionnaires: A Case Study

Learning Analytics techniques are widely used to improve students’ performance. Data collected from students’ assessments are helpful to predict their success and questionnaires are extensively adopted to assess students’ knowledge. Several mathematical models studying the correlation between students’ hidden skills and their performance to questionnaires’ items have been introduced. Among them, Non-negative matrix factorizations (NMFs) have been proven to be effective in automatically extracting hidden skills, a time-consuming activity that is usually tackled manually prone to subjective interpretations. In this paper, we present an intelligent data analysis approach based upon NMF. Data a…