Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

Domain-specific named entity disambiguation in historical memoirs

This paper presents the results of the extraction of named entities from a collection of historical memoirs about the italian Resistance during the World War II. The methodology followed for the extraction and disambiguation task will be discussed, as well as its evaluation. For the semantic annotations of the dataset, we have developed a pipeline based on established practices for extracting and disambiguating Named Entities. This has been necessary, considering the poor performances of out-of-the-box Named Entity Recognition and Disambiguation (NERD) tools tested in the initial phase of this work.Questo articolo presenta l’attività di estrazione di entità nominate realizzata su una collezione di memorie relative al periodo della Resistenza italiana nella Seconda Guerra Mondiale. Verrà discussa la metodologia sviluppata per il processo di estrazione e disambiguazione delle entità nominate, nonché la sua valutazione. L’implementazione di una metodologia di estrazione e disambiguazione basata su lookup si è resa necessaria in considerazione delle scarse prestazioni dei sistemi di Named Entity Recognition and Disambiguation (NERD), come si evince dalla discussione nella prima parte di questo lavoro

ModaFact: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection

Factuality and modality are two crucial aspects concerning events, since they convey the speaker`s commitment to a situation in discourse as well as how this event is supposed to occur in terms of norms, wishes, necessity, duty and so on. Capturing them both is necessary to truly understand an utterance meaning and the speaker`s perspective with respect to a mentioned event. Yet, NLP studies have mostly dealt with these two aspects separately, mainly devoting past efforts to the development of English datasets. In this work, we propose ModaFact, a novel resource with joint factuality and modality information for event-denoting expressions in Italian. We propose a novel annotation scheme, which however is consistent with existing ones, and compare different classification systems trained on ModaFact, as a preliminary step to the use of factuality and modality information in downstream tasks. The dataset and the best-performing model are publicly released and available under an open license

TimeFrame: Querying and Visualizing Event Semantic Frames in Time

In this work we introduce TimeFrame, an online platform to easily query and visualize events and participants extracted from document collections in Italian following a frame-based approach. The system allows users to select one or more events (frames) or event categories and to display their occurrences on a timeline. Different query types, from coarse to fine-grained, are available through the interface, enabling a time-bound analysis of large historical corpora. We present three use cases based on the full archive of news published in 1948 by the newspaper Corriere della Sera. We show that different crucial events can be explored, providing interesting insights into the narratives around such events, the main participants and their points of view

Italian Legislative Text Classification for Gazzetta Ufficiale

This work introduces a novel, extensive annotated corpus for multi-label legislative text classification in Italian, based on legal acts from the Gazzetta Ufficiale, the official source of legislative information of the Italian state. The annotated dataset, which we released to the community, comprises over 363,000 titles of legislative acts, spanning over 30 years from 1988 until 2022. Moreover, we evaluate four models for text classification on the dataset, demonstrating how using only the acts’ titles can achieve top-level classification performance, with a micro F1-score of 0.87. Also, our analysis shows how Italian domain-adapted legal models do not outperform general-purpose models on the task. Models’ performance can be checked by users via a demonstrator system provided in support of this work