Frame Semantics for Social NLP in Italian: Analyzing Responsibility Framing in Femicide News Reports
Authors: Gosse Minnema, Sara Gemelli, Chiara Zanchi, Viviana Patti, Tommaso Caselli, Malvina Nissim (University of Groningen, Università di Pavia, Università di Torino, 2021)
Topic: Lexical and Semantic Resources and Analysis

Abstract
We propose using a FrameNet-based approach for analyzing how socially relevant events are framed in media discourses. Taking femicides as an example, we perform a preliminary investigation on a large dataset of news reports and event data covering recent femicides in Italy. First, we revisit the EVALITA 2011 shared task on Italian frame labeling, and test a recent multilingual frame semantic parser against this benchmark. Then, we experiment with specializing this model for Italian and perform a human evaluation to test our model's real-world applicability. We show how FrameNet-based analyses can help to identify linguistic constructions that background the agentivity and responsibility of femicide perpetrators in Italian news.

Introduction
Frame semantics <cit.> is a theory of natural language understanding with a focus on word meanings (lexical units) and semantic roles (frame elements). The associated FrameNet project <cit.> has resulted in an extensive lexicon and annotated corpus implementing this theory. In the Italian computational linguistics community, there has also been considerable work on frame semantics, mostly focused on creating FrameNet resources <cit.>. However the practical usability of frame semantics for Italian is still largely unexplored. First of all,on automatic frame semantic parsing (FSP) <cit.>, which has seen considerable recent work on English <cit.>, there has not been any published work on Italian since the EVALITA-2011 shared task <cit.>. Second, a clear perspective on how computational frame semantics can be useful in real-life applications is still missing.We aim to advance the practical usability of frame semantics in Italian NLP in two ways. First, we test how well a recently developed multilingual model (LOME, xia-etal-2021-lome) for FSP performs on Italian. For this purpose we use existing data from the EVALITA 2011 campaign, which is the only reference for Italian on FSP, as well as new “real world" data collected in the context of the socially relevant domain of femicides. Second, we show how frame semantics can be used in practice to run analysis on real world data. From both efforts, we draw some recommendations for practical developments in Italian FSP.

Conclusion
We took initial steps towards addressing (i) the lack of recent frame semantic parsing models, and (ii) a missing perspective on how frame semantic analysis can be applied in practice.We adapted the multilingual LOME parser <cit.> to Italian, tested it against the EVALITA-2011 benchmark, and performed experiments to evaluate its real-world performance. Furthermore, we hypothesize that frame semantics can be a valuable analysis tool for analyzing backgrounding (and indirectly, blame attribution) of event participants, and propose news reports about femicides as an example of a domain where this type of analysis is very socially relevant. Our results indicate that LOME-based models can achieve acceptable performance, both on the EVALITA benchmark and out-of-domain on femicide reports, even without a large quantity of training data. We also found that a cross-lingual approach is useful: training on the concatenation of English and Italian data yields substantial improvements over using only Italian data, and even a zero-shot approach with only English data works quite well. However, our real-world performance analysis highlights key limitations of the Italian data: while models trained on EVALITA can achieve good frame detection performance, they fail when used `end-to-end', with predicate identification seemingly the main bottleneck. Finally, we performed a preliminary framing analysis of a large dataset covering femicides in Italy. While our analysis method is still in very early stages, we believe that our initial results demonstrate that frame semantics is meaningful for analyzing femicides and other social issues, and that it complements earlier construction-based approaches. In the future, we aim to expand our analysis system to make it usable for different social applications: for example, one could envision systems that can help social scientists test specific hypotheses about media reporting, help activists identify and highlight biased forms of reporting, or help make journalists more aware of their writing and its possible social-cognitive effects.