Emerging Trends in Gender-Specific Occupational Titles in Italian Newspapers
Authors: Pierluigi Cassotti, Andrea Iovine, Pierpaolo Basile, Marco De Gemmis, Giovanni Semeraro (Università di Bari Aldo Moro, 2021)
Topic: Gender and Inclusive Language Studies

Abstract
The grammatical gender system can influence the way the semantic gender is perceived. Italian is a grammatical gender language, in which nouns are classified for gender. In this work, we investigate the usage of gender-specific forms of occupational titles in a diachronic corpus of 3 billion tokens extracted from two Italian newspapers. The hypothesis is that the usage of gender-specific forms might be influenced by socio-cultural aspects, such as changes in the employment policy. We automatically collect a set of occupational titles and perform a diachronic analysis exploiting the frequency of gender-specific forms. Results show a correlation between changes in the usage of gender-specific forms and socio-cultural events.

Introduction
Throughout history, the prerogative use of specific gender forms over particular professions can fade away by introducing changes in the language lexicon (e.g., neologisms) or in the language usage (e.g., word frequencies).   The way the lexicon is affected by those changes depends on the grammatical gender system, i.e. the set of rules that define the agreement between noun classes forms and the other parts-of-speech. Grammatical gender systems can vary dramatically from one language to another. gygax2019 propose a classification of languages based on their grammatical gender system.        In this work, we focus on the Italian language, a grammatical gender language in which all nouns must be classified for gender. The Italian gender system admits three categories for nouns: gender-specific ending nouns, mobile gender nouns, and nouns where the gender is specified through determiners and adjectives <cit.>.  In gender-specific ending nouns, the gender forms are expressed through completely different lexical roots (e.g., genero/nuora).  In mobile gender nouns, the specific gender forms share the same lexical root, and the semantic gender is instead represented by different suffixes (e.g., scrittore/scrittrice). In other cases, the semantic gender of a noun is inferred only by the determiner and/or adjective (e.g., il giudice, la giudice). The peculiar characteristic found in the Italian language has strong repercussions in the way people refer to occupational titles, because a specific gender form might be preferred over the other due to historical reasons, regardless of the gender of the actual person being talked about <cit.>. This has become a hot-button issue in the last years, especially as a result of the United Nations Resolution Transforming our world: the 2030 Agenda for Sustainable Development with its global indicator framework for Sustainable Development Goals (SDGs), and specifically of SDG 5 Achieve gender equality and empower all women and girls (sub-goal 5.1 End all forms of discrimination against all women and girls everywhere) <cit.>. The objective of this paper is to monitor how the use of gender-specific occupational titles has changed in the Italian language over the years through the use of diachronic analysis tools. We would like to emphasize that the goal is not to map the composition of men and women for each profession over time, as this cannot be reliably inferred from text. Instead, we are interested in gauging the cultural relevance of the gender-specific titles over time, as reflected in the news domain. Accordingly, the contributions in this paper can be summarized as follows:  (i) We analyze emerging trends in the use of gender-specific occupational titles in the Italian language in a corpus of newspaper articles.[All data collected in this experiment is available here: <https://github.com/pierluigic/igsot>]   (ii) We perform a deep-dive analysis of the figures that have guided a significant shift for two professions in particular.  Large diachronic corpora have already been used to study social and cultural phenomenons that affected language in a significant way.  The Google Ngrams Dataset <cit.> is a dataset of n-grams extracted by 3.5 million books published between 1520 and 2008. DBLP:conf/dihu/AidenM11 exploit the huge quantity of information contained in the Google Ngrams Dataset to analyze the evolution of the language lexicon over time. In particular, the work offers interesting culturomics results, such as highlighting the spread of the term influenza during historical pandemic periods. DBLP:conf/acl/KutuzovVO17 exploit diachronic word embeddings to track wars and conflicts that took place from 1994 to 2010 all around the world. Diachronic word embeddings are trained on the English Gigaword news corpus <cit.> and used to predict conflict states: peace, war and stable.   laine2014linguistic analyze the linguistic sexism occurring in The Times newspaper over five decades (1965-2005), relying on the classification of linguistic sexism proposed in <cit.>. The authors hypothesize that occupational titles and agents would be more resistant to change than other forms of sexism over the decades. They confirm their hypothesis by exploring the frequencies of male and female affixes, showing that they keep stable. burr1995 performs an empirical analysis on manually-annotated occurrences of grammatical agents in a small synchronic corpus of Italian newspapers. The outcomes of this work lead the authors to conclude that women are underrepresented in Italian newspapers, especially in more high-position roles.

Conclusion
This paper investigates the usage of gender-specific forms of occupational titles in the Italian language in a diachronic corpus of 3 billion tokens extracted from two popular Italian newspapers. Through this analysis, we show that there are significant changes in the way newspaper articles refer to the masculine and feminine form of an occupational title and that they are consistent with socio-cultural events, such as changes in the employment policy. Moreover, we performed a more fine-grained analysis by extracting the most influential figures that have guided this shift for two occupational titles (male dancers and female poets). As future work, we propose to continue work on this field by increasing the size of the corpus and by including sources other than news, such as social media, job applications, and legal documents. This can help reduce any form of linguistic bias that may have been introduced by journalists and increase the significance of the results. Moreover, we will extend the list of occupational titles, as well as group titles together based on category. Finally, we propose to improve the process used to extract named entities that are associated with occupational titles in text.