Domain-specific Named Entity Disambiguation in Historical Memoirs
Authors: Marco Rovera, Federico Nanni, Simone Paolo Ponzetto, Anna Goy (Università di Torino, University of Mannheim, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
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.

Introduction
Current NLP techniques allow us to treat some types of historical textual resources provided by, among others, historical archives and libraries, as a source of information (and, in prospect, of knowledge) for automatic systems. Besides encyclopedic resources, libraries and archives provide many different types of texts, often spanning very specific geographical, individual or thematic contexts, for which current knowledge extraction systems may lack the suitable information. Nevertheless, the tasks of extracting, disambiguating and linking information provided by historical textual documents with respect to external knowledge bases is still a crucial step towards automatic access to written resources and for further employ of such knowledge in end-user applications (e.g. navigation, rich semantic search, creation of narrative chains). In order to address longer term tasks, such as event extraction from historical texts AUTHOR, we first addressed the task of extracting and disambiguating Named Entities (Persons, Locations and Organizations) from a corpus of historical memories of the \enquote{Liberation War} in Italy, during the Second World War. Due to the specificity of the domain and of the involved entities, state-of-the-art tools for Named Entity Recognition and Disambiguation show low performances, thus suggesting us to try to achieve our goal using a different approach. In this paper we present a collection of documents created by digitizing historical memoirs, together with an overview of the methodology we followed for the extraction and disambiguation of Persons, Locations and Organizations, as well as the results of the evaluation of its output in comparison with the output of two state-of-the-art systems. The outline of the paper is the following: in Section 2 some related projects are discussed, while in Section 3 the dataset used in the experiment is presented. Section 4 describes the test of two automatic NER tools (4.1) and the methodology devised for our experiment (4.2). In Section 5 the results of the evaluation are discussed, while Section 6 concludes the paper and outlines the next developments of the project.

Conclusion
In this paper we presented an ongoing work aimed at performing Named Entity Disambiguation on a digitized historical corpus, along with the results of the evaluation. Further steps will be a) the refinement of the presented method by means of weighting measures on co-occurrence and possibly of feature optimization techniques, b) the application of the tested disambiguation strategy also to LOC and ORG entities, as well as the study of a cross-category disambiguation strategy, and finally c) the extension of the corpus and of the gazetteers in order to obtain a larger coverage of the domain. Furthermore, this work represents the first step for extracting events and their participants from the presented corpus.