Word Embeddings in Sentiment Analysis

In the late years sentiment analysis and its applications have reached growing popularity. Concerning this field of research, in the very late years machine learning and word representation learning derived from distributional semantics field (i.e. word embeddings) have proven to be very successful in performing sentiment analysis tasks. In this paper we describe a set of experiments, with the aim of evaluating the impact of word embedding-based features in sentiment analysis tasks.Recentemente la Sentiment Analysis e le sue applicazioni hanno acquisito sempre maggiore popolarità. In tale ambito di ricerca, negli ultimi anni il machine learning e i metodi di rappresentazione delle parole che derivano dalla semantica distribuzionale (nello specifico i word embedding) si sono dimostrati molto efficaci nello svolgimento dei vari compiti collegati con la sentiment analysis. In questo articolo descriviamo una serie di esperimenti condotti con l’obiettivo di valutare l’impatto dell’uso di feature basate sui word embedding nei vari compiti della sentiment analysis

TAG-it@ EVALITA 2020: Overview of the Topic, Age, and Gender Prediction Task for Italian

The Topic, Age, and Gender (TAG-it) prediction task in Italian was organised in the context of EVALITA 2020, using forum posts as textual evidence for profiling their authors. The task was articulated in two separate subtasks: one where all three dimensions (topic, gender, age) were to be predicted at once; the other where training and test sets were drawn from different forum topics and gender or age had to be predicted separately. Teams tackled the problems both with classical machine learning methods as well as neural models. Using the training-data to fine-tuning a BERT-based monolingual model for Italian proved eventually as the most successful strategy in both subtasks. We observe that topic and gender are easier to predict than age. The higher results for gender obtained in this shared task with respect to a comparable challenge at EVALITA 2018 might be due to the larger evidence per author provided at this edition, as well as to the availability of pre-trained large models for fine-tuning, which have shown improvement on very many NLP tasks

Linguistic Profile of a Text and Human Ratings of Writing Quality: a Case Study on Italian L1 Learner Essays

This paper presents a study based on the linguistic profiling methodology to explore the relationship between the linguistic structure of a text and how it is perceived in terms of writing quality by humans. The approach is tested on a selection of Italian L1 learners essays, which were taken from a larger longitudinal corpus of essays written by Italian L1 students enrolled in the first and second year of lower secondary school. Human ratings of writing quality by Italian native speakers were collected through a crowdsourcing task, in which annotators were asked to read pairs of essays and rated which one they believed to be better written. By analyzing these ratings, the study identifies a variety of linguistic phenomena spanning across distinct levels of linguistic description that distinguish the essays considered as ‘winners’ and evaluates the impact of students’ errors on the human perception of writing quality

DARC-IT: a DAtaset for Reading Comprehension in Italian

In this paper, we present DARC-IT, a new reading comprehension dataset for the Italian language aimed at identifying ‘question-worthy’ sentences, i.e. sentences in a text which contain information that is worth asking a question about. The purpose of the corpus is twofold: to investigate the linguistic profile of question-worthy sentences and to support the development of automatic question generation systems.In questo contributo, viene presentato DARC-IT, un nuovo corpus di comprensione scritta per la lingua italiana per l’identificazione delle frasi che si prestano ad essere oggetto di una domanda2. Lo scopo di questo corpus è duplice: studiare il profilo linguistico delle frasi informative e fornire un corpus di addestramento a supporto di un sistema automatico di generazione di domande di comprensione