Elisabetta Fersini


SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
Elisabetta Fersini | Francesca Gasparini | Giulia Rizzi | Aurora Saibene | Berta Chulvi | Paolo Rosso | Alyssa Lees | Jeffrey Sorensen
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem.The collection and annotation is described for the task dataset.The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.


Cross-lingual Contextualized Topic Models with Zero-shot Learning
Federico Bianchi | Silvia Terragni | Dirk Hovy | Debora Nozza | Elisabetta Fersini
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models. Models have to be either single-language or suffer from a huge, but extremely sparse vocabulary. Both issues can be addressed by transfer learning. In this paper, we introduce a zero-shot cross-lingual topic model. Our model learns topics on one language (here, English), and predicts them for unseen documents in different languages (here, Italian, French, German, and Portuguese). We evaluate the quality of the topic predictions for the same document in different languages. Our results show that the transferred topics are coherent and stable across languages, which suggests exciting future research directions.

OCTIS: Comparing and Optimizing Topic models is Simple!
Silvia Terragni | Elisabetta Fersini | Bruno Giovanni Galuzzi | Pietro Tropeano | Antonio Candelieri
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: https://github.com/MIND-Lab/OCTIS.

An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures
Silvia Terragni | Elisabetta Fersini
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a single-objective Bayesian optimization. This allows us to determine the robustness of a topic model for several evaluation metrics. We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other.


Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models
Silvia Terragni | Debora Nozza | Elisabetta Fersini | Messina Enza
Proceedings of the First Workshop on Insights from Negative Results in NLP

Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.

Profiling Italian Misogynist: An Empirical Study
Elisabetta Fersini | Debora Nozza | Giulia Boifava
Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language

Hate speech may take different forms in online social environments. In this paper, we address the problem of automatic detection of misogynous language on Italian tweets by focusing both on raw text and stylometric profiles. The proposed exploratory investigation about the adoption of stylometry for enhancing the recognition capabilities of machine learning models has demonstrated that profiling users can lead to good discrimination of misogynous and not misogynous contents.


SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
Valerio Basile | Cristina Bosco | Elisabetta Fersini | Debora Nozza | Viviana Patti | Francisco Manuel Rangel Pardo | Paolo Rosso | Manuela Sanguinetti
Proceedings of the 13th International Workshop on Semantic Evaluation

The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.


A Multi-View Sentiment Corpus
Debora Nozza | Elisabetta Fersini | Enza Messina
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Sentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usually investigate independently each aspect, i.e. Subjectivity Classification, Sentiment Polarity Classification, Emotion Recognition, Irony Detection. In this paper we present a Multi-View Sentiment Corpus (MVSC), which comprises 3000 English microblog posts related the movie domain. Three independent annotators manually labelled MVSC, following a broad annotation schema about different aspects that can be grasped from natural language text coming from social networks. The contribution is therefore a corpus that comprises five different views for each message, i.e. subjective/objective, sentiment polarity, implicit/explicit, irony, emotion. In order to allow a more detailed investigation on the human labelling behaviour, we provide the annotations of each human annotator involved.

TWINE: A real-time system for TWeet analysis via INformation Extraction
Debora Nozza | Fausto Ristagno | Matteo Palmonari | Elisabetta Fersini | Pikakshi Manchanda | Enza Messina
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at http://twine-mind.cloudapp.net/streaming.