JRC-Names: Multilingual entity name variants and titles as Linked Data

Abstract

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 their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/‫ﺒﻨﻴﺎﻤﻴﻥ‬ Netanyahu/Netanjahu/Nétanyahou/Netahny/Нетаньяху/‫.)ﻨﺘﻨﻴﺎﻫﻭ‬ This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

“…JRC-TMA-CC is a hybrid system combining a rule-based approach and machine learning techniques. It is a corpus-driven system, lightweight and highly multilingual, exploiting both automatically created lexical resources, such as JRC-Names (Ehrmann et al, 2017), and external resources, such as BabelNet (Jacquet et al, 2019a). The main focus of the approach is on generating the possible inflected variants for known names (Jacquet et al, 2019b RIS is a modified BERT model, which uses CRF as the top-most layer (Arkhipov et al, 2019).…”

A Finnish news corpus for named entity recognition

“…However, we discarded this class during the second annotation stage since person titles are not a commonly included category in named entity annotation. They can be, however, found for example in related work on semantic webs (Ehrmann et al, 2017).…”

JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task

“…We submitted four system instance results, all of which are based on our in-house NER system NERONE (Ehrmann et al, 2017;Steinberger et al, 2015), which we describe first.…”