\Il Piave mormorava...\": Recognizing Locations and other Named Entities in Italian Texts on the Great War"
Authors: Lucia C. Passaro, Alessandro Lenci (Università di Pisa, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Increasing amounts of sources about World War I (WWI) are nowadays available in digital form. In this paper, we illustrate the automatic creation of a NE-annotated domain corpus used to adapt an existing NER to Italian WWI texts. We discuss the annotation of the training and test corpus and provide re-sults of the system evaluation.

Introduction
Increasing amounts of sources about World War I (WWI) are nowadays available in digital form. The centenary of the Great War is also going to foster this trend, with new historical sources being digitized. This wealth of digital documents offers us an unprecedented possibility to achieve a multidimensional and multiperspectival insight on war events, understanding how soldiers and citizens of different countries and social conditions experienced and described the events in which they were involved together, albeit on opposite fronts and with different roles. Grasping this unique opportunity however calls for advanced methods for the automatic semantic analysis of digital historical sources. The application of NLP methods and tools to historical texts is indeed attracting growing interest and raises interesting and highly challenging research issues (Piotrowsky 2012). The research presented in this paper is part of a larger project dealing with the digitization and computational analysis of Italian War Bulletins of the First World War (for details see Boschetti et al. 2014). In particular, we focus here on the domain and language adaptation of a Named Entity Recognizer (NER) for Italian. As a byproduct of this project, we illustrate the automatic creation of a NE-annotated domain corpus used to adapt the NER to the WWI texts. War bulletins (WBs) were issued by the Italian Comando Supremo “Supreme Headquarters” during WWI and WWII as the official daily report about the military operations of the Italian armed forces. They are plenty of Named Entities, mostly geographical locations, often referring to small places unmarked in normal geographic maps or with their name changed during the last century because of geopolitical events, hence hardly attested in any gazetteer. To accomplish the Named Entity Recognition task, several approaches have been proposed such as Rule Based Systems (Grover et al., 2008; Mikheev et al., 1999a; Mikheev et al., 1999b), Machine Learning based (Alex et al., 2006; Finkel et al., 2005; Hachey et al., 2005; Nissim et al., 2004, including HMM, Maximum Entropy, Decision Tree, Support Vector Machines and Conditional Random Field) and hybrid approaches (Srihari et al., 2001). We used a Machine Learning approach to recognize NEs. Rule-based systems usually give good results, but require long development time by expert linguists. Machine learning techniques, on the contrary, use a collection of annotated documents for training the classifiers. Therefore the development time moves from the definition of rules to the preparation of annotated corpora. The problems of the NER in WWI bulletins are larger than those encountered in modern texts. The language used in such texts is early 20th century Italian, which is quite different from contemporary Italian in many respects and belongs to the technical and military jargon. These texts are therefore difficult to analyze using available Italian resources for NER, typically based on contemporary, standard Italian. Grover et al. (2008) describe the main problems encountered by NER systems on historical texts. They evaluated a rule-based NER system for person and place names on two sets of British Parliamentary records from the 17th and 19th centuries. One of the most important issues they had to deal was the gap between archaic and contemporary language. This paper is structured as follows: In section 2, we present the CoLingLab NER and in section 3 we describe its adaptation to WWI texts.

Conclusion
Location names play an important role in histori-cal texts, especially in those - like WBs - describ-ing the unfolding of military operations. In this paper, we presented the results of adapting an Italian NER to Italian texts about WWI through the automatic creation of a new NE-annotated corpus of WBs. The adapted NER shows a significantly increased ability to identify Locations. In the near feature, we aim at processing other types of texts about the Great War (e.g., letters, diaries and newspapers) as part of a more general project of information extraction and text mining of war memories.