Arretium or Arezzo? A Neural Approach to the Identification of Place Names in Historical Texts
Authors: Rachele Sprugnoli (Fondazione Bruno Kessler, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents the applicaEnglish. This paper presents the application of a neural architecture to the identiﬁcation of place names in English historical texts. We test the impact of different word embeddings and we compare the results to the ones obtained with the Stanford NER module of CoreNLP before and after the retraining using a novel corpus of manually annotated historical travel writings.

Introduction
Named Entity Recognition (NER), that is the automatic identiﬁcation and classiﬁcation of proper names in texts, is one of the main tasks of Natural Language Processing (NLP), having a long tradition started in 1996 with the ﬁrst major event dedicated to it, i.e. the Sixth Message Understanding Conference (MUC-6) (Grishman and Sundheim, 1996). In the ﬁeld of Digital Humanities (DH), NER is considered as one of the important challenges to tackle for the processing of large cultural datasets (Kaplan, 2015). The language variety of historical texts is however greatly different from the one of the contemporary texts NER systems are usually developed to annotate, thus an adaptation of current systems is needed. In this paper, we focus on the identiﬁcation of place names, a speciﬁc sub-task that in DH is envisaged as the ﬁrst step towards the complete geoparsing of historical texts, which ﬁnal aim is to discover and analyse spatial patterns in various ﬁelds, from environmental history to literary studies, from historical demography to archaeology (Gregory et al., 2015). More speciﬁcally, we propose a neural approach applied to a new manually annotated corpus of historical travel writings. In our experiments we test the performance of different pre-trained word embeddings, including a set of word vectors we created starting from historical texts. Resources employed in the experiments are publicly released together with the model that achieved the best results in our task1.

Conclusion
Conclusions and Future Works Conclusions and Future Works In this paper we presented the application of a neural architecture to the automatic identiﬁcation of place names in historical texts. We chose to work on an under-investigated text genre, namely travel writings, that presents a set of speciﬁc linguistic features making the NER task particularly challenging. The deep learning approach, combined with in-domain training set and in-domain historical embeddings, outperforms the linear CRF classiﬁer of the Stanford NER module without the need of performing feature engineering. Annotated corpus, best model and historical word vectors are all freely available online. As for future work, we plan to experiment with a ﬁner-grained classiﬁcation so to distinguish different types of locations. In addition, another aspect worth studying is the georeferencing of identiﬁed place names so to map the geographical dimension of travel writings in Italy. An example of visualisation is given in Figure 1 where the locations automatically identiﬁed from the test ﬁle taken from the book “Naples Riviera” are displayed: place names have been georeferenced using the Geocoding API7 offered by Google and displayed through the Carto8 web mapping tool. Another interesting work would be the detection of itineraries of past travellers: this application could have a potential impact on the tourism sector, suggesting historical routes alternative to those more beaten and congested and making tourists rediscovering sites long forgotten.