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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents

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Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. ...

Springer2020

CLEF-HIPE-2020 Shared Task Named Entity Datasets

Maud Ehrmann, Matteo Romanello

CLEF-HIPE-2020 (Identifying Historical People, Places and other Entities) is a evaluation campaign on named entity processing on historical newspapers in French, German and English, which was organized in the context of the impresso project and run as a CL ...

2020

The Unstoppable Rise of Computational Linguistics in Deep Learning

James Henderson

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...

Association for Computational Linguistics2020

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Maud Ehrmann, Matteo Romanello

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of ...

Springer International Publishing2020

RSVQA: Visual Question Answering for Remote Sensing Data

Devis Tuia, Sylvain Lobry

This article introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information, which can be useful for a wide range of tasks, including land cover classification, object counting, or de ...

2020

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Christian O'Reilly, Elisabetta Iavarone

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousa ...

Springer2019

Named Entity Processing for Historical Texts

Maud Ehrmann, Matteo Romanello

Recognition and identification of real-world entities is at the core of virtually any text mining application. As a matter of fact, referential units such as names of persons, locations and organizations underlie the semantics of texts and guide their inte ...

2019

JRC-Names: Multilingual Entity Name variants and titles as Linked Data

Maud Ehrmann

Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and t ...

2016

A Method for Record Linkage with Sparse Historical Data

Maud Ehrmann, Yannick Rochat, Giovanni Colavizza

Massive digitization of archival material, coupled with automatic document processing techniques and data visualisation tools offers great opportunities for reconstructing and exploring the past. Unprecedented wealth of historical data (e.g. names of perso ...

2016

Named Entity Resources - Overview and Outlook

Maud Ehrmann

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and t ...

European Language Resources Association (ELRA)2016