In recent years, event processing has become 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: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work and can have an impact both in the fields of Natural Language Processing and Digital Humanities. Our work aims at shedding light on the complex concept of events adopting an interdisciplinary perspective. More specifically, theoretical and practical investigations are carried out on the specific topic of event detection and classification in historical texts by developing and releasing new annotation guidelines, new resources and new models for automatic annotation.

Event Detection and Classification for the Digital Humanities

Sprugnoli, Rachele
2018

Abstract

In recent years, event processing has become 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: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work and can have an impact both in the fields of Natural Language Processing and Digital Humanities. Our work aims at shedding light on the complex concept of events adopting an interdisciplinary perspective. More specifically, theoretical and practical investigations are carried out on the specific topic of event detection and classification in historical texts by developing and releasing new annotation guidelines, new resources and new models for automatic annotation.
2018
Inglese
Tonelli, Sara
Università degli studi di Trento
TRENTO
213
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/91833
Il codice NBN di questa tesi è URN:NBN:IT:UNITN-91833