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A Method for Record Linkage with Sparse Historical Data

Publications associées (39)

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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

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an extended 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 ...

CEUR-WS2020

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

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

Index-Driven Digitization and Indexation of Historical Archives

Maud Ehrmann, Giovanni Colavizza

The promise of digitization of historical archives lies in their indexation at the level of contents. Unfortunately, this kind of indexation does not scale, if done manually. In this article we present a method to bootstrap the deployment of a content-base ...

2019

Detecting Trends in Job Advertisements

Pierre Dillenbourg, Kshitij Sharma, Khalil Mrini

We present an automatic method for trend detection in job ads. From a job-posting website, we collect job ads from 16 countries and in 8 languages and 6 job domains. We pre-process them by removing stop words, lemmatising and performing cross-domain filter ...

2017

deepschema.org: An Ontology for Typing Entities in the Web of Data

Karl Aberer, Michele Catasta, Panagiotis Smeros, Amit Gupta

Discovering the appropriate type of an entity in the Web of Data is still considered an open challenge, given the complexity of the many tasks it entails. Among them, the most notable is the definition of a generic and cross-domain ontology. While the onto ...

2017

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

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

Kamusi has been developing a system to analyze texts on the source side and present users with sense-specified dictionary options. Similarly to spellcheck, the user selects the intended meaning. We then use a multilingual lexical database to bridge to matc ...

ENeL2016