Novel Event Detection and Classification for Historical Texts

Rachele Sprugnoli, Sara Tonelli

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Event processing is an active area of research in the Natural Language Processing community but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts particularly in the current era of massive digitization of historical sources: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorised into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry so far underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus and new models for automatic annotation.
Original languageEnglish
Pages (from-to)229-265
Number of pages37
JournalComputational Linguistics
Volume45
DOIs
Publication statusPublished - 2019

Keywords

  • computational linguistics

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