EVALITA 2016: Overview of the 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian

Basile, Pierpaolo; Cutugno, Franco; Nissim, Malvina; Patti, Viviana; Sprugnoli, Rachele

doi:10.4000/books.aaccademia.1924

Cited by 15 publications

(13 citation statements)

References 6 publications

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“…8 A special thanks to Angelo Basile (angelo.basile@symanto.net), Junior Research Scientist at Symanto, for providing the models transformed into PyTorch and directly shareable through Transformers released by huggingface.co: https://huggingface.co/. 9 https://huggingface.co/m-polignano-uniba/ bert_uncased_L-12_H-768_A-12_italian_alb3rt0 (Barbieri et al 2016) carried out at EVALITA 2016 (Basile et al 2016), a challenge on sentiment analysis on Italian tweets. The second is the hate speechannotated corpus released for the HaSpeeDe shared task (Bosco et al 2018) held at EVALITA 2018 (Caselli et al 2018), a challenge on the detection of hateful content in Italian social media.…”

Section: Evaluation Of Alberto On Nlp Tasksmentioning

confidence: 99%

AlBERTo: Modeling Italian Social Media Language with BERT

Polignano¹,

Basile²,

Basile³

et al. 2019

ijcol

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Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demonstrating how they can obtain very encouraging results in challenging NLP tasks by generalizing the properties of natural language directly from the data. Models such as ELMo, GPT/GPT-2, BERT, ERNIE, and RoBERTa have proved to be extremely useful in NLP tasks such as entailment, sentiment analysis, and question answering. The availability of these resources mainly in the English language motivated us towards the realization of AlBERTo, a natural language model based on BERT and trained on the Italian language. We decided to train AlBERTo from scratch on social network language, Twitter in particular, because many of the classic tasks of content analysis are oriented to data extracted from the digital sphere of users. The model was distributed to the community through a repository on GitHub and the Transformers library (Wolf et al. 2019) released by the development group huggingface.co. We have evaluated the validity of the model on the classification tasks of sentiment polarity, irony, subjectivity, and hate speech. The specifications of the model, the code developed for training and fine-tuning, and the instructions for using it in a research project are freely available.

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Section: Evaluation Of Alberto On Nlp Tasksmentioning

confidence: 99%

AlBERTo: Modeling Italian Social Media Language with BERT

Polignano¹,

Basile²,

Basile³

et al. 2019

ijcol

Self Cite

View full text Add to dashboard Cite

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“…A portion of the data overlaps with data from NEEL-IT (Basile et al, 2016a), PoSTWITA and FacTA (Minard et al, 2016). See (Basile et al, 2016b) for details.…”

Section: Development and Test Datamentioning

confidence: 99%

“…The chairs of EVALITA 2016 10 introduced in the organization of the new campaign novel elements, aimed at addressing most of the issues raised by both the questionnaires and the panel (Basile et al, 2016b).…”

Section: Towards Future Editionsmentioning

confidence: 99%

“…In this paper we present the solution we propose for the NEEL-IT task (Basile et al, 2016b) of Evalita 2016(Basile et al, 2016a. The task consists of annotating each named entity mention (characters, events, people, locations, organizations, products and things) in an Italian Tweet text, linking it to DBpedia nodes when available or labeling it as NIL entity otherwise.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

sisinflab: an ensemble of supervised and unsupervised strategies for the NEEL-IT challenge at Evalita 2016

Cozza¹,

Bruna²,

Noia³

2016

EVALITA. Evaluation of NLP and Speech Tools for Italian

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“…Not surprisingly, most applications of this kind are based on English. Italian is used much less frequently (a few examples are Bosco, Patti, and Bolioli 2013;Bosco et al 2014;Bosco, Patti, and Bolioli 2015;Barbieri et al 2016), although there has been some evaluation of Italian NLP tools and resources (Attardi et al 2015;Basile et al 2016).…”

Section: Related Workmentioning

confidence: 99%

Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis

Farías¹,

Lai²,

Patti³

et al. 2019

DemRes

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BACKGROUND Demographers are increasingly interested in connecting demographic behaviour and trends with 'soft' measures, i.e., complementary information on attitudes, values, feelings, and intentions. OBJECTIVE The aim of this paper is to demonstrate how computational linguistic techniques can be used to explore opinions and semantic orientations related to parenthood. METHODS In this article we scrutinize about three million filtered Italian tweets from 2014. First, we implement a methodological framework relying on Natural Language Processing techniques for text analysis, which is used to extract sentiments. We then run a supervised machine-learning experiment on the overall dataset, based on the annotated set of tweets from the previous stage. Consequently, we infer to what extent social media users report negative or positive affect on topics relevant to the fertility domain. RESULTS Parents express a generally positive attitude towards being and becoming parents, but they are also fearful, surprised, and sad. They also have quite negative sentiments about their children's future, politics, fertility, and parental behaviour. By exploiting geographical information from tweets we find a significant correlation between the

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EVALITA 2016: Overview of the 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian

Cited by 15 publications

References 6 publications

AlBERTo: Modeling Italian Social Media Language with BERT

AlBERTo: Modeling Italian Social Media Language with BERT

sisinflab: an ensemble of supervised and unsupervised strategies for the NEEL-IT challenge at Evalita 2016

Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis

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