Guidelines for annotating the LUNA corpus with frame information

This document defines the annotation workflow aimed at adding frame information to the LUNA corpus of conversational speech. In particular, it details both the corpus pre-processing steps and the proper annotation process, giving hints about how to choose the frame and the frame element labels. Besides, the description of 20 new domain-specific and language-specific frames is reported. To our knowledge, this is the first attempt to adapt the frame paradigm to dialogs and at the same time to define new frames and frame elements for the specific domain of software/hardware assistance. The technical report is structured as follows: in Section 2 an overview of the FrameNet project is given, while Section 3 introduces the LUNA project and the annotation framework involving the Italian dialogs. Section 4 details the annotation workflow, including the format preparation of the dialog files and the annotation strategy. In Section 5 we discuss the main issues of the annotation of frame information in dialogs and we describe how the standard annotation procedure was changed in order to face such issues. Then, the 20 newly introduced frames are reported in Section 6

A Smell is Worth a Thousand Words: Olfactory Information Extraction and Semantic Processing in a Multilingual Perspective (Invited Talk)

More than any other sense, smell is linked directly to our emotions and our memories. However, smells are intangible and very difficult to preserve, making it hard to effectively identify, consolidate, and promote the wide-ranging role scents and smelling have in our cultural heritage. While some novel approaches have been recently proposed to monitor so-called urban smellscapes and analyse the olfactory dimension of our environments (Quercia et al., 2015), when it comes to smellscapes from the past little research has been done to keep track of how places, events and people have been described from an olfactory perspective. Fortunately, some key prerequisites for addressing this problem are now in place. In recent years, European cultural heritage institutions have invested heavily in large-scale digitisation: we hold a wealth of object, text and image data which can now be analysed using artificial intelligence. What remains missing is a methodology for the extraction of scent-related information from large amounts of texts, as well as a broader awareness of the wealth of historical olfactory descriptions, experiences and memories contained within the heritage datasets. In this talk, I will describe ongoing activities towards this goal, focused on text mining and semantic processing of olfactory information. I will present the general framework designed to annotate smell events in documents, and some preliminary results on information extraction approaches in a multilingual scenario. I will discuss the main findings and the challenges related to modelling textual descriptions of smells, including the metaphorical use of smell-related terms and the well-known limitations of smell vocabulary in European languages compared to other senses

Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It?

Generation-based data augmentation (DA) has been presented in several works as a way to improve offensive language detection. However, the effectiveness of generative DA has been shown only in limited scenarios, and the potential injection of biases when using generated data to classify offensive language has not been investigated. Our aim is that of analyzing the feasibility of generative data augmentation more in-depth with two main focuses. First, we investigate the robustness of models trained on generated data in a variety of data augmentation setups, both novel and already presented in previous work, and compare their performance on four widely-used English offensive language datasets that present inherent differences in terms of content and complexity. In addition to this, we analyze models using the HateCheck suite, a series of functional tests created to challenge hate speech detection systems. Second, we investigate potential lexical bias issues through a qualitative analysis on the generated data. We find that the potential positive impact of generative data augmentation on model performance is unreliable, and generative DA can also have unpredictable effects on lexical bias

A 3D Role-Playing Game for Abusive Language Annotation

Gamification has been applied to many linguistic annotation tasks, as an alternative to crowdsourcing platforms to collect annotated data in an inexpensive way. However, we think that still much has to be explored. Games with a Purpose (GWAPs) tend to lack important elements that we commonly see in commercial games, such as 2D and 3D worlds or a story. Making GWAPs more similar to full-fledged video games in order to involve users more easily and increase dissemination is a demanding yet interesting ground to explore. In this paper we present a 3D role-playing game for abusive language annotation that is currently under development

An Analysis of Abusive Language Data Collected through a Game with a Purpose

In this work we present an analysis of abusive language annotations collected through a 3D video game. With this approach, we are able to involve in the annotation teenagers, i.e. typical targets of cyberbullying, whose data are usually not available for research purposes. Using the game in the framework of educational activities to empower teenagers against online abuse we are able to obtain insights into how teenagers communicate, and what kind of messages they consider more offensive. While players produced interesting annotations and the distributions of classes between players and experts are similar, we obtained a significant number of mismatching judgements between experts and players

Prendo la Parola in Questo Consesso Mondiale: A Multi-Genre 20th Century Corpus in the Political Domain

In this paper we present a multigenre corpus spanning 50 years of European history. It contains a comprehensive collection of Alcide De Gasperi’s public documents, 2,762 in total, written or transcribed between 1901 and 1954. The corpus comprises different types of texts, including newspaper articles, propaganda documents, official letters and parliamentary speeches. The corpus is freely available and includes several annotation layers, i.e. key-concepts, lemmas, PoS tags, person names and geo-referenced places, representing a high-quality ‘silver’ annotation. We believe that this resource can foster research in historical corpus analysis, stylometry and computational social science, among others